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### AuthorTopic: University of Melbourne - Subject Reviews & Ratings  (Read 1042960 times) Tweet Share

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#### QuantumJG

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##### Re: University of Melbourne - Subject Reviews & Ratings
« Reply #585 on: July 05, 2016, 09:29:07 pm »
+2
Subject Code/Name: ENGR20004 Engineering Mechanics

Workload:  3 x 1 hour lectures, 1 x 2 hour workshops

Assessment:  2 mid-semester tests (15%), weekly online quizzes (5%), 4 group assignments (30%), 3 hour exam (50%)

Lectopia Enabled:  Yes, with/without screen capture

Past exams available:  Yes, two. There's also practice problems

Textbook Recommendation:  No prescribed texts, but workshops take questions from: Meriam JL and Kraige LG, Engineering Mechanics : Dynamics 7th Edition, HGibbeler RC, Statistics and Mechanics of Materials 3rd Edition

Lecturer(s): Professor Joe Klewicki, Dr David Ackland, Prof Ivan Marusic

Year & Semester of completion: 2016, Semester 1

Rating:  3/5

Firstly, this subject is not going to be a walk in the park. This subject packs a lot of foundational statics and dynamics into one semester. Doing the assignments in a group will either add a level of complexity to the subject, or make things easier. Choose wisely during the first workshop, as that will be your group for the entire semester. I happened to find myself in a group with people who would send me their work right at the end (handwritten of course), so I'd spend many nights trying to type up the work to resemble something professional.

Make sure you go to workshops and jot down the worked solutions the tutors go through. Those solutions to problems will help you learn how to tackle problems. Also, the online quizzes generally tend to be questions from the previous week's tutorial, so make sure you understand those questions so you can ace those quizzes for an easy 5%.

The mid-semester tests aren't too bad, but make sure you check your multiple choice answers, as it's very easy to make a calculation error that yields a similar, but wrong answer.

The dynamics component is probably twice as difficult as the statics component, so make sure you maximise your grades in the statics component. The dynamics component in the exam is rather difficult, so make sure you really know your statics, so you can get some easy marks. Know the difference between method of joints, and method of sections.
2008: Finished VCE

2009 - 2011: Bachelor of Science (Mathematical Physics)

2012 - 2014: Master of Science (Applied Mathematics/Mathematical Physics)

2016 - 2018: Master of Engineering (Civil)

Semester 1:[/b] Engineering Mechanics, Fluid Mechanics, Engineering Risk Analysis, Sustainable Infrastructure Engineering

Semester 2:[/b] Earth Processes for Engineering, Engineering Materials, Structural Theory and Design, Systems Modelling and Design

#### QuantumJG

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##### Re: University of Melbourne - Subject Reviews & Ratings
« Reply #586 on: July 05, 2016, 09:47:39 pm »
+2
Subject Code/Name: CVEN30008 Engineering Risk Analysis

Workload:  2 x 1 hour lectures, 1 x 1 hour tutorials

Assessment:  A 2 hour exam (60%), 2 group assignments (30%) and tutorial attendance (10%)

Lectopia Enabled:  Yes, with/without screen capture

Past exams available:  No.  Sample quantitative questions provided

Textbook Recommendation:  There are prescribed texts, but I didn't find them necessary.

Lecturer(s): Dr Lihai Zhang along with guest lecturers

Year & Semester of completion: 2016, Semester 1

Rating:  3/5

This subject is useful for anyone wanting to become an engineer, as it makes you think about all the risks present on the worksite and how to mitigate them to safe levels. The subject starts off with qualitative risk analysis, which seems to rely on guesstimating risks through commonsense. It's irritating for someone like me who likes numbers and dealing with quantitative results.

For this component of the subject, we had an assignment where we had to come up with a risk analysis of a project. It seemed quite overwhelming, but tutors were lenient.

The next component dealt with quantitative risk analysis, which was basically learning about applying probability theory. We looked over normal, binomial, Poisson and t-distributions. We then looked at things such as confidence intervals, hypothesis tests, and linear regression.

We then had an assignment where we applied this to a mine, to determine how to minimise excavation costs and maximise safety.

The exam is equally split among the qualitative component and quantitative component. The quantitative component is really easy to prepare for, however the qualitative part is harder, as it's not covered that much in tutorials. This is what caused me to haemorrhage marks in the exam. I'd argue rote-learning the lectures (especially the points in AS/NZS ISO 31000:2009 risk standard).
2008: Finished VCE

2009 - 2011: Bachelor of Science (Mathematical Physics)

2012 - 2014: Master of Science (Applied Mathematics/Mathematical Physics)

2016 - 2018: Master of Engineering (Civil)

Semester 1:[/b] Engineering Mechanics, Fluid Mechanics, Engineering Risk Analysis, Sustainable Infrastructure Engineering

Semester 2:[/b] Earth Processes for Engineering, Engineering Materials, Structural Theory and Design, Systems Modelling and Design

#### silverpixeli

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##### Re: University of Melbourne - Subject Reviews & Ratings
« Reply #587 on: July 06, 2016, 11:57:13 pm »
+5
Subject Code/Name: BMEN20001 Biomechanical Physics and Computation

This is a relatively new subject, 2016 semester 1 marked its second offering. The intended audience is biomed students looking to master the physics foundations necessary for studying biomechanics.

The subject's aim is to serve as a combination of ENGR20004 Engineering Mechanics and COMP20005 Engineering Computation, embedded in a biomechanics context. That is, we studied the same topics but where engineering students might focus on steel bars supporting loads we'll look at human limbs holding weights. We also spent some time considering the complications that arise when analysing organic materials such as bone and skin.

I wasn't a member of the intended audience, as a Physics and Computing student interested in extending and applying what I already know into a new context. For someone like me, this subject wasn't that bad. It acted as an interesting extension of VCE Physics topics (the 'mechanics' core AoS and the 'structures and materials' detailed study), plus an introduction to yet another programming language (MATLAB).

However, as a subject aimed at introducing biomed students to physics and computation, BMEN20001 has a fair way to go. The assignments needed to be more properly thought-out, and they needed to provide all necessary information at release time. The demonstrators were shocking, and the workshops were possibly worse than useless --- the MATLAB learning experience was liable to scare anyone new to computing away from the field for good! The pacing of the content was out of step with the impression given by the handbook entry; more attention could have been paid to the foundational concepts (or even just more careful attention; it's just very important to avoid introducing confusion at that level because it underlies the rest of the entire subject and indeed all of Classical Mechanics!).

That's the reasoning behind the score I have awarded this subject. However, I have confidence that the coordination team will be able to improve the subject in future iterations by responding to student feedback.

Three 1h lectures (less on average, however; see comments!)
One 2h workshop (sometimes a tutorial, sometimes a MATLAB class)

Assessment:

5% - Assignment 1
10% - Assignment 2
10% - Assignment 3
15% - Assignment 4, groups of 4

10% - Mid-semester test
50% - 3h exam in exam period

Lecture Capture: Yep

Past exams available:
There were no past exams available because all of the previous exam questions had been included as tutorial questions (only one previous exam this time round).

Lecturer:

The content lectures were presented by Vijay Rajagopal.

Additionally, there was a guest lecture from one of the tutors, a series of three guest lectures on head injury by Andrew Short, and a series of three further guest lectures from various biomechanics researchers.

Year & Semester of completion: 2016 Semester 1

Rating: 3/5

Technologies:

This subject served as an (admittedly poor) introduction to programming using MATLAB. Learning MATLAB was the focus of most of the workshops, and the first three assignments were mostly programming.

Textbook Recommendation:

There were four recommended textbooks, from which the course material was drawn. These were:
- Humphrey JD, and Delange SL, An Introduction to Biomechanics
- Nihat O. Nordin M, Goldsheyder D, and Leger D, Fundamentals of Biomechanics, 3rd Edition
- Meriam Jl and Kraige LG, Engineering Mechanics: Dynamics, 7th Edition
- Hibbeler RC, Statics and Mechanics of Materials, 3rd Edition

I personally used a very old edition of Hibbeler (like 1991 or something, I think it's technically not even the same book as it was just called 'Mechanics of Materials') and I found it absolutely fantastic, but that's coming from a strong maths and physics background and with a preference for comprehensive textbooks that leave no ambiguity and don't shy away from complexity. It was also a regular engineering text, with no biomechanical context. Nevertheless, it was an enlightening read; one of those textbooks that answers all of your questions as soon as they form, and also really gives you a big-picture view of the material.

Some of the other texts might serve as a better introduction to these physics concepts, but they seemed to also assume a bit of a background in biology, of which I had none. Therefore, I recommend the Hibbeler option for anyone with my background.

The rest of this review is pretty long. I've included a lot of detail about the topics we covered, the class experience, and the assessments. I hope it aids people in their decisions about this subject, and helps people taking it / required to take it know what kind of experience to expect. Feel free to shoot me a PM if you have any further questions about this subject.

Content

The 'physics' in 'biomechanical physics and computation' stands for statics (the study of materials undergoing forces but not moving), and dynamics (the study of objects in motion). These topics were the focus of the lectures. The 'computation' represents an approach to these problems that utilises computers, which can do the grunt work of crunching numbers, repeating calculations and plotting graphs. This was the focus of the workshops. Of course, everything was looked at through a biomechanics perspective.

In terms of statics, we quickly whizzed through the basics of forces and moments (a.k.a. torques, shout out to VCE Physics students) and then looked at the much more confusing topic of internal forces and moments (like within bones or at joints). After that was a look at the concepts of stress and strain (normal and shear), extending to applications of forces in 3D, as well as other material properties (especially those relevant to biological materials like skin). We were going to discuss beams but skipped that topic and went straight to axial loading, and finally looked at thin-walled spherical and cylindrical pressure vessels (with examples including arteries and aneurysms!).

For dynamics, again we quickly touched on the basics of motion and relative motion of particles - position, velocity, and acceleration. We also looked quickly at work and energy. We then spent some time on linear momentum (for collision analysis, feat. concepts like the coefficient of restitution), and then on rotational motion and angular momentum. Finally, we had a brief look at rigid bodies (bodies larger than a particle, where rotation matters) and some more powerful and general equations of motion that can be used to analyse rigid body systems.

Despite the appearances from the handbook entry, I think this subject was pitched a little too fast as an introduction to these physics concepts. I think for someone completely new to mechanics (statics and dynamics), a bit much was assumed and the basics/foundational points were skimmed over. I would recommend that someone in this position try very hard to master the basics before tackling the later topics, and I think resources such as khanacademy's high school motion playlists would be perfect for this purpose.

The 'computation' part spanned everything from variables and data types to control structures (branching and looping) and functions. Despite the presence in the handbook, numerical methods for solving differential equations were not explored. Numerical simulations were carried out, but they were for simple projectile motion situations. This was the domain of the majority of the workshops, which I'll talk more about later.

Lectures

Vijay's lectures weren't bad, but I'd say as a relatively new lecturer he's definitely got some improvement to do in terms of presenting these concepts. This semester, there were a few topics that seemed to be lost on students. One was a matrix method for converting between 3D stresses and strains, but this was identified and I think Vijay is going to use a much more intuitive approach next time. Another (this wasn't so much the feeling of the cohort but just from me) tricky point that was neglected was the difference between 'tensorial shear strain' and 'engineering shear strain'. It turns out that tensorial shear strain (ε - used in the matrix equations), actually has twice the value of engineering shear strain (γ - used in other formulas). This subtle point was hidden in the definitions of the matrix equations on the slides and I feel like more attention could have been drawn to it, and a very clear distinction made between the shear strains.

Apart from a few tricky points like this, it's easy to see why Vijay always looks so happy and cheerful when he's lecturing - you can tell he loves sharing his passion for biomechanics with people and teaching the concepts too. He was very kind, friendly, and approachable. His priority was everyone's understanding, which is why he always went out of his way to give the best explanations possible (e.g. repeating a lecture or two in response to student questions). Because his heart is demonstrably in the right place, I have no doubt that he'll continually improve his teaching quality in semesters to come.

Vijay also adopted the unconventional (but effective, IMO) strategy of offering lectures dedicated to working through problems, as a demonstration of how to apply the equations and laws we were learning. This was good because, particularly in physics, getting into the process of using theory to solve problems is just as important as getting your head around the theory in the first place. I am glad that this importance was reflected in the lecture schedule.

The other thing about these problem classes, along with the large number of guest lectures (7 in total) and the mid-sem test, was that it left a reduced number of content-lectures. From 3 lectures a week, there ended up being 18 assessable content lectures, making for a fairly light revision load.

Workshops

Unfortunately, the workshops for this subject were quite a let down. Since I came with prior knowledge in computing, I was able to ignore the explanations of the foundational programming concepts. I fear, however, that other students were not so lucky. Nothing that was presented in the workshops was useful beyond what a short online MATLAB (or any other programming language) tutorial would offer, and some of the explanations were outright incorrect and potentially damaging to students' basic understanding of programming concepts.

I don't have faith that the tutors will improve in future semesters (unless they are replaced). If I'm right, then for anyone new to computing and interested in taking this subject, I recommend treating everything presented in the workshops as suspect. Then, I'd advise taking the list of content presented and seeking alternate resources for learning those things. I didn't get a chance to explore it, but I'd be willing to bet that this online course from MIT (the small part of it that is relevant) runs rings around these guys. Short of that, you can PM me on ATAR Notes to help explain a piece of MATLAB code. You can search on Google. Anything other than trusting the demonstrators with your impressions about computing - it's such a wonderful area of study and it really hurt me to see it presented like it was.

A few of the workshops were not allocated to MATLAB, but instead to worked examples, kinda like in the lectures. Due to the general incompetence of the demonstrators, this was equivalent to working through the problems alone or with peers. This does bring me to a small point of confusion, though. Each week, topical 'tutorial' questions were uploaded. Sometimes, a selection of these questions were covered in an example-lecture. Other weeks, the workshop was dedicated to working through them. Either way, the tutorial sheets were just a source of application questions (with accompanying solutions, of varying correctness) even though there was no official 'tutorial'.

Assignments

The first three assignments involved a mechanics problem followed by a short MATLAB scripting task. The problem was that the instructions weren't clear, and often the information given was not really enough to complete the tasks. In several cases, LMS announcements came out afterwards clearing up some ambiguous or missing points, but I think it's really important to minimise this kind of thing - obviously assignment tasks should be as complete as possible upon release so that students are not disadvantaged in the time they have to complete them waiting for things to be cleared up. Also, any assumptions made due to missing information should not later by invalidated by LMS announcements. These issues generally didn't affect me, but they made the assignments come across as incredibly unpolished overall. I hope that in the future these little glitches can be ironed out before assignment release, as much as possible.

Then came the fourth assignment. In about week 7 it was announced that the final assignment would involve group work, which was the first we had heard of it. During week 9, a set of three guest lectures on head impact and head injury was presented by guest-lecturer Andrew Short. That week, the workshop was replaced with a fake-head-dropping experiment, after which we were ushered into groups of 4 students and vaguely told that the final assignment would be a four-part practical report based on these experiments.

Later, more detailed information (almost in the form of an assignment specification!) surfaced. The report was to have four parts:
- a literature review on impact testing,
- a report on the experimental procedure and equipment,
- and analysis of the results,
- and a discussion of the findings and any compliance issues with respect to an impact testing standards document we were given.
Most of the expectations were ambiguous. Some of the specification was incomprehensible. I'm pretty sure the mark allocations didn't even total to 40 (which they were supposed to).

Within my group, I took primary responsibility for the analysis section since nobody else really wanted to use MATLAB. This section, in particular, was a shambles. Part of the task was to analyse the motion of a fake head as it fell and then bounced on different materials. This was meant to be conducted by manually tracking some dots attached to the head form in video recordings of the drop tests. This frame-by-frame tracking (as demonstrated in the week 10 workshop) was quite a lengthy and tedious process, and we were looking for a way to automate it. In the week 11 workshop, we were provided with some MATLAB scripts that attempted to track the dots for us. However, these scripts were very fragile, and even after repeated updates released by Andrew, failed to analyse most of the videos without crashing. I ended up making my own steamlined version of the manual tracking script and using that instead.

In the final week (much to my frustration, for the same reasons as above), Andrew took the LMS discussion forums to construct an FAQ list, detailing assignment advice (mostly things that should have been in the initial specification). At this point, it was far too late to perform our analysis again and so we just had to stick with our manually-acquired data. We did manage to incorporate some advice into the other sections. However, it just shouldn't have been expected that we would be completing the majority of our work in the final week!

Worst of all, one night before the due date, another (apparently, finally working) version of the analysis script was released. Other required scripts were still broken and I never had a chance to try the new analysis script but I have a hunch that it wouldn't actually have been free of bugs. Either way, attempting to release the required material for the assignment the night before the due date when it should have worked from the beginning really frustrated and upset me.

I think the entire cohort felt miserable about the impossibly frustrating final assignment. I can't imagine it will survive the SES reviews unchecked, and I sincerely hope that it is improved for future iterations of the subject.

The assessment was plagued by other small administrative issues such as the due-dates not being announced with the assignments, and LMS resubmissions before the due-date being accidentally disallowed on 3/4 of the assignments (those administered by the demonstrators, ha) just caused a lot of unnecessary stress.

MST and Exam

Though tutorial and lecture examples were a mix of typical engineering examples and simplified biomechanical examples, the questions on the MST and final exam were largely drawn from a biomechanical context. Both papers were  well-balanced in terms of time and difficulty, with the majority of questions being similar to those found in lecture examples or tutorial sheets.

I made an effort to work through every tutorial question before the exam and was rewarded for that this preparation by an exam with no surprises. However, the difficulty of the final exam questions was a little greater than that of most tutorial questions. I'm certainly glad we had three hours because I needed all of it to complete the paper!

« Last Edit: July 07, 2016, 12:28:33 am by silverpixeli »
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#### silverpixeli

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##### Re: University of Melbourne - Subject Reviews & Ratings
« Reply #588 on: July 07, 2016, 12:27:53 am »
+7
Subject Code/Name: COMP30024 Artificial Intelligence

This subject is core to the OLD Computing and Software Systems Major. Unless you enrolled a year or two ago, you'll be in the new one which doesn't have this as a core subject (but of course you can still take it as an elective). It runs in semester 1.

COMP30024 was basically an introduction to the very broad field of artificial intelligence. We learned a whole heap of generic approaches but didn't really go super deep into any of the more specific areas like machine learning or natural language processing. There are lots of masters subjects for those purposes, though, and we did get to spend some time on robotics which was cool!

The first half felt a lot like the good days of COMP20007 Design of Algorithms, learning clever algorithms for AI problems. The second half felt like a nice extension of high-school probability, with some other interesting topics thrown in (decision-making and robotics) according to the lecturer's research interests.

Overall, I can't really fault this subject on anything, and compared to subjects I have scored 5/5 in the past, it's at least as good (maybe I should try to be a little harder to impress!). COMP30024 was seamlessly coordinated, with very clear expectations. The assignments were challenging but very engaging and rewarding, and the exam was similarly challenging but not unfair.

Highly recommended as a third year elective for anyone with a love for algorithms (and/or probability) and any interest in the field of AI. You'll come away suitably grounded in the field with a firm grasp of which way is up, ready and excited to continue studying AI at masters level.

Two 1h lectures
One 1h tutorial (it was a 2hr tutorial on the timetable, but we only used the first hour - the second was offered for project work)

Assessment:
8% - Project Part A (groups of 2)
22% - Project Part B (same groups)
0% - Unmarked 'feedback quiz' in-lecture (instead of a MST)
70% - 2h exam in exam period

Lecture Capture: Y

Past exams available:
There were no past exams available, but one sample exam with solutions was released.

Lecturer:

The first half (symbolic AI) was presented by Sarah Erfani

The second half (AI under uncertainty) was presented by Chris Leckie

Year & Semester of completion: 2016 Semester 1

Rating: 5/5

Technologies:

There wasn't a huge focus on implementation, except in the project, which had to be written in Java. Java was assumed knowledge, and putting together a workable solution required a decent level of proficiency within the team (but nothing more than what you'd pick up from, say, SWEN20003 Object-Oriented Software Design).

Textbook Recommendation:

The recommended textbook is Artificial Intelligence: A Modern Approach by Russel and Norvig. It's quite good! I actually bought a copy (albeit a cheap international edition from abebooks) and I really got a lot out of it. It's quite well written and goes into a very satisfying amount of detail.

The rest of this review goes into a lot of detail about the subject experience -- content, classes and assessment. Hopefully it helps you know what kind of thing to expect if you decide to take this subject.

Content

Artificial Intelligence can be defined as the process of responding to perceptions in a rational manner. That is, to maximise the expected result according to some performance measure. The first week was spent formalising these kinds of ideas, with a few definitions to base our study on.

From there, the subject was split into two stages. First, we talked about acting rationally in the world where everything is visible, predictable, and discrete (symbolic AI). Topics in this area included
- tree/graph search - from depth first search to A* (but not necessarily on a finite, known graph as you might be familiar with from prerequisites - instead, often on an infinite search space that you generate on the fly)
- adversarial search - includes strategies for searching when you don't get to control all of the moves, so you have to consider all of the possibilities the opponents might select.
- learning (for game playing) - this was a very light taste of machine learning, aimed at inspiring people to have a go with their projects. We looked at different learning methodologies and their effective use in building a game-playing AI.
- constraint satisfaction - searching for solutions in a complex state space and using really powerful general-purpose techniques to improve our search strategies. We looked at backtracking search with a host of improvements, as well as gradient descent search and a few important special cases.

Then, when Chris Leckie took over the lectures, we moved on to reasoning and acting rationally in environments where there is uncertainty in your perceptions and uncertainty in the results of your actions, or in an environment where there are multiple other agents with competing goals and they are acting on hidden information. We covered
- a short introduction to uncertainty, which was basically VCE Methods probability but with slightly different notation
- Bayesian inference - representing complex probability distributions with many factors using a Bayesian network to save space, and performing calculations within this representation
- auctions - we discussed a few considerations that come up when trying to design a fair auctioning system, which is an example of a multi-agent environment where ideally you want everyone's interests to be satisfied but you can only rely on people following their own self-interest
- robotics - we discussed a few more considerations that come up when you're dealing with uncertain inputs and uncertain outputs: 'there are only two sources of uncertainty in robotics: everything your robot senses, and everything your robot does'. We also looked at a few other interesting robotics concepts as well.

There was also a non-examinable deep learning guest lecture to finish it all off!

Lectures

I believe it was Sarah's first time lecturing a subject. I have seen some very poor lecturers over the past few years and to Sarah's credit she did fairly well and I think she'll be able to continually improve and become a great lecturer - she's clearly very intelligent (able to accurately interpret and articulately answer student questions) and knowledgeable (able to clearly explain points and stress important subtleties). The only thing her lectures lacked was student engagement, to the point where I found the lectures a great guide for what to focus on but spent my study time with the textbook instead (which happens to be very well written and approachable). I do think that as Sarah gains experience she will build confidence and become a lot more engaging!

Lectures with Chris Leckie were also very clear and insightful, but were a lot more engaging. Chris is a very articulate speaker (I said during his introduction in the first lecture 'this guy's voice is so clear, I will be able to break 3.0x speed when I watch his recordings', and I was right). He also injects a healthy level of sarcastic humour into his lectures and that's my kind of humour.

The lectures themselves were structured nicely. Even if sometimes the individual slides weren't arranged so logically, it was always easy to tell what topics were important and considered 'part of the course', and equally easy to find more information in the textbook (for all topics except TD-Leaf(λ), a machine learning algorithm, which is not in the textbook and for which I recommend checking out the original paper, referenced from the slides --- though it's a bit of a tough read). It was made very clear to us what level of understanding was expected, and there was a slide at the end of each topic's lecture material that described what the key points were, with examples of how they could be examined. After taking the exam, I can say that at least this year these summary slides were fairly accurate (though by all other accounts, that doesn't mean the exam questions were actually easy to complete!).

Another cool feature of the slides was that often there were frame-by-frame animations of the execution of certain algorithms, and I've always been a big fan of that kind of visualisation technique for presenting a tricky algorithm, so that was great.

Tutorials

Well, let me start by saying that the tutorials for this subject saved my semester. Drowning due to the excessive workload of SWEN30006 (see that review) and just generally overloaded due to other commitments I fell drastically behind in this subject. After mastering the first 4 weeks over the mid semester break (which was after week 4 this year), I didn't touch the remaining lectures until swotvac. The assignment and the tutorials were all the AI I participated in until then. And, well, the tutorials with the head tutor Lida were just fantastic!

I was able to come in, listen to Lida's quick but comprehensive review of the subject matter (the first time I was hearing about these concepts), and engage with it right away; answering and asking questions and really getting a feel for the ideas. It was only one hour each week but it really kept me above the surface in this subject. I didn't have time to properly study the topics until swotvac but it all came together very nicely at that point because of the amazing introductions I had received at the tutorials.

I missed out on two of the tutorials, and I really regret doing so. One time, I caught it up by going to another tutor's class, but it just didn't compare to Lida's classes. If you are taking this subject, my advice is to figure out which tutorials she is taking, and make yourself available at that time. Bonus if you can go to a late one that has a small number of students because that's what we had, and it was more personal. I really wish I had been able to keep up with this subject during the semester because I would have been able to get so much more than an introduction out of these tutorials, it really could have been great.

Assignments

The project (part A and B) for this subject was to make an AI to play a game called 'hexifence', in groups of two. Hexifence is a board game identical to dots and boxes (a.k.a. paddocks), except with a hexagonal grid. Part A was a simple primer project, it involved reading, storing and analysing a board state. In part B, the reigns were off and we were tasked with implementing a full AI to play this game from start to finish using whatever strategies or AI techniques we wanted. Beyond marks for code quality and correctness, the majority of the marks were allocated to the 'competitiveness' of our AI (as tested against several other agents of varying prowess) and the 'creativity' of our approach (to encourage independent research and experimentation). A H1 score, we were told, required far more than the basic adversarial search strategy from lectures.

The options were nearly endless. There were a whole lot of suggested paths we didn't bother to go down, such as using machine learning to fine tune our AI's move evaluation abilities. There were entire branches of promising theory not studied that we didn't have time to explore (Monte-Carlo tree search, negamax and other improved adversarial search algorithms). It was just a matter of pouring over AI papers about how this algorithm or that adjustment had performed amazingly in chess or go AI programs, and seeing if we understood it enough to try to implement it ourselves.

We ended up having more ambition than time, and put together a street-fighting AI that cut corners in its move searches by using a myriad of game-specific knowledge to eliminate unlikely possibilities (perhaps closer to how a human plays a board game, than the traditional brute-force AI approach). We were rewarded for our host of hacks and heuristics with full marks in all categories! You can view our submission on GitHub (beware of academic honesty if you are thinking of taking inspiration for your own AI project).

One thing that the assignments lacked was satisfying closure in the form of a student v student tournament. To rectify this lack of bloodshed, I'm currently writing a networked multi-player hexifence server and client-programs that I plan to distribute so that my peers can plug in their AIs and face them off against each other like some sick animal fight but with computers. Its kind of a mix between everything I've learned this semester in COMP30024, COMP30023, and SWEN30006. I'll definitely keep this post updated with how it all goes, including results if we manage to run a tournament.

Exam

There was no MST for this subject, but instead a mid-semester 'feedback quiz' in one of the lectures, which was optional and did not contribute to our marks. I planned to go but ended up studying in the systems garden instead, but from the recording it was basically a few short questions of an introductory calibre, followed by a discussion of the solutions as a class. The questions were said to be typical of about the difficulty of half of the questions on the final exam, and that there were also another 30% of questions that would be a little harder, and the remaining questions 'quite challenging'.

The final exam ended up being pretty fair, though I've heard others found it  challenging. The questions were essentially what we were told to expect on the summary slides of each topic, though whether that meant they were easy to complete correctly or not was another matter. There was a mix of multiple choice, short answer and extended response questions, and also one or two 'carry out this algorithm' style problems. Basically, the summary slides were a really good guide, and there were no real surprises.

At least one exam question came from the textbook chapter questions. I know because I happened to complete that chapter question in the day or two before the exam. Therefore, I think it might be a good idea to at least give some thought to these questions throughout the semester (hopefully worked solutions wouldn't be too hard to find for your edition).

« Last Edit: June 20, 2019, 10:56:39 am by silverpixeli »
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#### silverpixeli

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##### Re: University of Melbourne - Subject Reviews & Ratings
« Reply #589 on: July 07, 2016, 11:26:05 am »
+5
Subject Code/Name: COMP30023 Computer Systems

This subject is core to the Computing and Software Systems Major of the BSci. It runs in semester 1. It could have been called COMP30023 Operating Systems and Networks, because that's basically what it's about (plus some Security topics).

There's a big step up in expectations from even second year computing subjects like COMP20007 Design of Algorithms in how dirty you have to get your hands in the practical computing stuff. There's certainly no hand-holding; it was really our responsibility to play around with and explore every new concept, and at times the lectures were more of a preview than an educational resource.

Due to the shear amount of highly-detailed content covered, and the inherent complexity of the concepts studied, this subject was pretty difficult! Embracing the ever-steep learning curves of technological topics was really the focus of this subject. In fact, a lot of my study was finding a rabbit hole and then jumping straight down. I personally found this exploration very rewarding, and I guess that's why I'm still here in a computing degree in third year. Being in a cohort of people who mostly felt the same way made for a really interesting subject.

So the 4.5 score comes from a minor gripe I had about the slightly 'confused' purpose of the lectures. Other than that, this subject was an absolute blast and I really wish I had more of a chance to appreciate the projects and topics we were doing - it all ended up a little rushed because of my workload this semester, and this subject suffered.

Otherwise, it was incredibly well coordinated, with super engaging content, labs and assignments (which make you feel like you can do anything!), and a very friendly and passionate lecturer. Hopefully you will have more time to engage with this subject if you decide to take it.

Two 1h lectures (this year they were both on Friday morning!)
One 1h tutorial
One 1h computer lab

Assessment:
15% - Assignment 1
10% - Mid semester test
15% - Assignment 2
60% - 2h exam in exam period

Lecture Capture: Y

Past exams available:
There were no past exams available, but one sample exam (with no solutions) was released. Same for the MST.

Lecturer: Michael Kirley (more later)

Year & Semester of completion: 2016 Semester 1

Rating: 4.5/5

Technologies:

A very technical subject; a host of different technologies were utilised and explored.

expand

You'll want access to a unix machine, as it was the operating system of study. A Linux install would be best for a seamless experience. I haven't jumped down that rabbit hole yet; I got through fine with OSX with only a few minor road bumps that were easily overcome in most cases (brew install ...). Windows users can use MinGW as they probably have in previous subjects. Also, Windows 10 is going to have native linux integration soon (look up 'windows subsystem for linux') and so that would probably get you through.

It's important to have access to a unix environment because you will need to explore and know about the workings of several command-line utilities (also, the assignments  need to run on the university servers, which run linux). A fantastic tutorial which I nearly made it through can be found here.

There's plenty of programming, almost all of it in C. You'll learn to use a lot of new functions, but you'll want to brush up on your multi-file C programs and Makefiles for the assignments.

Version control (and submission) for the assignments was done using SVN (Subversion). This may change to git in the future, but either way version control is very easy to pick up by following an online tutorial or two. I love doing it all on the command line but my friend recommends TortoiseSVN for those who need a GUI.

Python was used in a few labs for exploring some security concepts such as buffer overflow and cryptography. All the information needed was provided, but you'll want to have some way to use python (even a browser-based python IDE will do, though).

Textbook Recommendation:

There were lots of recommended textbooks, due to the split nature of the course between operating systems topics and and networking topics. I've also described personal recommendations along with the official recommendations.

expand

For operating systems, the lecturer often referenced two popular textbooks: Modern Operating Systems by Tanenbaum, and Operating Systems Concepts by Silberschatz et al. (a.k.a. the 'Dinosaur textbook'). I didn't check out Tanenbaum but I read a few chapters from Silberschatz and I found it pretty good, but maybe a little bit lacking in detail.

As with most computing topics, the Internet is a great resource. I'd like to thank Wikipedia in particular for always having a page on the OS concept I was trying to grapple with. You'll never be hungry for details while you have an internet connection!

For the networking component, the two reference textbooks were Computer Networks, by Tanenbaum once again, and Computer Networking: A Top-Down Approach by Kurose. I didn't try either of these myself but Tanenbaum had a lot of nice diagrams that made it into the slides.

Instead, I relied instead on the internet again. Wikipedia is pretty good with its networking articles but they can sometimes skip details or focus on things that aren't really relevant to this subject. I managed to find a pretty great resource that was as comprehensive as I'd ever need in The TCP/IP Guide, a nasty-looking little web-book that you can probably find a free pdf for (not that I would know). It's quite a tome but it spared no detail and left very few questions unanswered. I recommend picking it up before swotvac. Well, I recommend reading it alongside the lectures on those topics! Let's not get into why I was watching those lectures for the first time during swotvac.

Another focus of the subject is on exploring real live operating systems, specifically unix operating systems like linux and OSX. This exploration took place in two ways: exploring command-line utilities (like 'ls'), and implementing programs in C that talk directly to the operating system (instead of talking to it through standard libraries). For command-line utilities, your unix machine has a built in manual for all of the programs it can run, and this is the recommended text for exploring them (type 'man ls' to see what I mean. Type 'man man' to learn how to use the manual!). For implementation in C, sometimes there are also helpful manual pages for the functions you're practising with. Other times, you'll need to search on Google and find a good Stack Overflow thread because, well, someone has asked your question already and people have answered it already. Otherwise, you can look through the rest of the search results and find a tutorial (or a web-hosted man page).

One particularly helpful tutorial for me was Beej's Guide to Network Programming Using Internet Sockets. This was linked from the slides but goes into a lot more detail than required, and really helped with the second assignment which involved network programming (along with multi-threading and other things, but this helped with the network part!). In this area, the man pages are particularly dense, and Beej has a very approachable explaining style that made the details not so devilish.

There was one final official reference text, and that was Advanced Programming in the Unix Environment. It's meant to cover all the programming stuff in a lot of detail but I never went there. Might be good to check out as a reference if internet searching doesn't solve your problem.

The rest of this review is pretty extensive, I've included a lot of detail about the whole experience. I hope it helps you know what to expect if you're taking this subject.

Content

I want to give an brief overview of the content, in the hopes that it will help people reading this to know what to expect and to piece it all together as it's happening. As is often the case, I feel like it's a lot easier to see how the topics relate looking back from the end of the course compared to what it was like looking forward from the beginning.

We can start with operating systems. Operating systems comprised the first half (or so) of the course. Our study of operating systems could have been broken down into the following topics:

- process management: the concept of executing a program; getting the processor to follow its instructions. We also paid a lot of attention to the idea of the kernel, the part of the operating systems that provides a lot of functionality to programs who want to access hardware (network interfaces, input devices, storage devices, etc) (and does a lot more)

- process scheduling: answering questions like 'how does the operating system switch between running programs to make it look multiple programs are running at the same time, given that a processor can only do one thing at a time?' and 'how does the operating systems decide how much time processes should get running on the processor?'

- memory management: how do we allocate RAM to running programs? this topic dealt with strategies for making sure every program has enough memory to do its thing, and how to make sure it can't access anyone else's memory. We looked at solutions like 'a paging based virtual-memory system' which will make sense after you do some research on it

- file systems: how does an operating system make use of permanent storage? usually, by collecting certain disk bytes into 'files'. It turns out that in unix, a file is JUST data, and you put all the metadata (size, location, etc) in a separate structure called an 'inode'. This topic was all about that, and about what you do next, like how you store and use directories. We also touched on other file systems and the idea of a virtual file system which hides this all behind a consistent layer of abstraction

- synchronization: when you have multiple programs running at once and the OS could decide to switch between them at any point in time, you need to be very careful in what your programs assume. e.g. 'this value wont change between the time I read from it and the time that I write to it when I'm writing i++ in my C-program' might not actually be the case if another process is also reading/writing that variable. More accurately, you're in danger when you have multiple threads which are just multiple paths of execution through the same program, which can all possibly alter global variables and interfere with each other. This topic was about ways of dealing with these issues; making sure your threads execute safely in a 'synchronized' manner. We learned about all sorts of approaches to concurrency control including mutual exclusion (which is hard to get right!) and these things called semaphores which let us do more complex synchronization tasks.

Then, the course branched off into networks-land. Basically we spent a lot of time talking about the different layers of protocols and functions that make the internet work. This was broken down into the following topics:

- network fundamentals: this was the introduction to the networking portion of the course. We got a broad overview of the networks topic, and there was a lot to talk about but the main takeaway was the idea of a conceptual networking model like the OSI model some might be familiar with from VCE IT: Software Development, which is an idea for how to structure network components to promote flexibility and compatibility. In practice people use the TCP/IP model (most notably as the underlying structure of the internet).

- security: kind of a miscellaneous topic, and always a background focus throughout the study of operating systems, we then took some time to focus on more traditional security topics: symmetric and asymmetric encryption, digital signatures, and digital certificates. The emphasis was on understanding the structure of the schemes and not so much on the details of the various encryption and hashing algorithms in use.

- network/internet layer: we then dove into the first TCP/IP layer of our study; the internet layer. We looked in detail at the structure of an IP packet, the process of fragmenting packets to send them over small transmission lines, the concepts underlying the various historical IP addressing schemes, and also the mechanics of routing packets about a network.

- transport layer: built on the internet layer (which probably uses IP, but doesn't need to) are two common transport-layer protocols; UDP and TCP. Both involve addressing mechanisms to make sure you know which program you're talking to at the target IP address (using ports), but TCP builds on this by adding a few helpful features. One is delivery guarantees, through a system of sending acknowledgements when you receive TCP segments, and resending segments if you don't get an acknowledgement before a certain time limit (in case they didn't make it the previous time(s)). It also adds congestion control, which is where TCP hosts dynamically adjust the size of their outgoing data segments in response to the rate at which they miss acknowledgements. That was the kind of stuff studied in this topic.

- application layer: finally was the application layer. Built atop of TCP (or UDP, depending on your needs) are a bunch of useful application protocols such as HTTP for requesting and sending web pages, DNS for translating domain names into IP addresses, and Mail protocols, well, for email. These were studied in some depth in this final section.

In each of the topics studied, there were roughly three separate focuses.

First, the conceptual understanding of the topic; think short-answer questions about explaining various processes or comparing various strategies.

Then, there was the specific knowledge of certain strategies, tested by small worked examples like 'here's a set of processes that need to be scheduled, what would happen if we used a round-robin scheduling algorithm?' - We were humans simulating and OS.

Finally, we were always looking to our computers to try these things out and see what was going on. We built programs in C (and Python for the security stuff) and played around with bash utilities to explore the things we were learning about in action.

Lectures

The lectures were perhaps the only downside of this subject, and they weren't that bad I just think they could have been a little better.

At the lectures we really felt the whole 'no hand-holding; I'm telling you about this stuff but I'm not really helping you to understand how it works'. I'm not sure that Michael Kirley was aware of this, because he seemed to think he was explaining things, but often he just glossed over things and finished sentences with 'what's happening' and 'what's going on' as a substitute for what was actually happening. Thus, instead of a learning resource, lectures were often more of a topic outline, and it was left to us to go and read the textbook sections and browse the internet and play with our computers to really understand what was happening.

To be fair, there was so much content in this subject that with 2 lectures a week (and 2 lectures lost to Good Friday) it really would have been impossible to teach all this content in the lectures, I just think that they shouldn't be marketed as a learning resource and that way the lecturer could have been a lot more clear about what was expected, if that was the established purpose of the lecture.

One quirk that I think everyone enjoyed was how often he gave away exam questions, literally saying 'I can guarantee you that this question will be on your final exam. I cannot be more explicit than that' or one of a thousand variations of that. I wrote all of the things he said down, and there was far too much for one exam and mid semester test. However, most of what was on the exam was something that he had said, and so listening paid off.

Tutorials

Pretty standard - we sat in a room and discuss topical questions based on the material from the previous week's lectures. I would recommend having seen the previous week's lectures at that point for the purpose of the tutorial. I only managed to be that up-to-date for one tutorial, and it was awesome.

Tutorial questions were a pretty good judge of whether you were doing enough reading outside of lectures. Watching the lectures themselves wasn't quite enough to answer all the questions, which was a subtle indication that more was expected from us. These trends didn't really follow as much into the MST and exam, but I guess the tutorials were there to really engage those students who wanted a little more knowledge out of the subject. I found them pretty interesting overall.

I actually found it helpful to go to two tutorials back to back, to get both tutors' perspectives and to hear everything twice, because sometimes in the first tutorial there wasn't enough time to really get everything. This way, I had one less hour between classes, but I got a second chance to take notes and ask questions (also, felt like a boss answering the difficult questions in the second tutorial after discussing the answers in the first ).

Labs

My computer labs were a bit of a shambles because the lab demonstrator basically quit the job two weeks in and so we didn't have any help. It wasn't a big problem, because the demonstrators were there only for emergencies anyway. No hand-holding, remember? I couldn't go to any other time and so The Friday 5.15 session (which was officially cancelled, but we still went along) became a mix between a weekly chill sesh in EDS6, and a small group of us actually trying to work on the lab tasks for the week (which usually took us more than the one hour).

In reality, I ended up finding time a few weeks after each lab to nut them out. I didn't get around to all of them. The labs were quite challenging, and quite fun! They demonstrated the kind of technical exploration that we were expected to be doing in our own time alongside the study of more conceptual operating systems concepts.

Assignments

The assignments in this subject were challenging but great fun. They required a lot of work, and an ability to brave the dark corners of C, but they were immensely rewarding. After making a process scheduling simulator and a multi-threaded, networked game server in C, I can say that I feel powerful! I can't wait to continue building cool things, and I have a small list of projects starting with a mail server and working my way up to an entire operating system. When I find the time, I am going to have a lot of fun building these things.

The marking for the assignments was very pedantic in that you were dramatically punished in your grades if you did not follow all instructions. Some people's code compiled fine on the lab machines but not on the school of engineering servers due to different compiler settings, and they lost almost all of their marks even though their project worked. This year, for assignment one, Michael went to the effort to help a lot of these students out by fixing these small compatibility issues and assessing them on the merits of their otherwise working solution, minus only a few penalty points. But he really shouldn't have had to do that, and he was not so kind about assignment two; it was very clear that it was our responsibility to make sure things worked. I imagine this will be very clear in future years also, so test your code!

MST and Exam

The mid semester test was pretty brutal. The cohort's highest mark was 18/20. I thought I had done fairly well, but I got a 14.5. A lot of my friends then said they went to consultation and they had been very strict marking the short-answer questions. Luckily I went to consultation and found they had mis-totalled my paper and I actually got 17.5, which was more like what I expected: I lost one mark on an explanation I fluffed my way through, one mark for a silly mistake in multiple choice, and half a mark for not knowing exactly what the -F flag on the bash ls command does! That's another point about how brutal the MST was: It hadn't been clear how much we were meant to play around with bash commands like ls, and reading their man pages to see the different options, so this obscure question was a bit of a surprise. My advice is to play around with all the commands you see in labs and lectures, and not to rote learn their options, but to have a play and try to figure out and be able to explain how they work.

The exam was pretty fair. Michael was very clear about its structure, outlining the topics covered in each of the 5 sections and the mark-breakdown of questions. There were some questions that went into quite a lot of detail about a particular topic, so my strategy of doing a lot of external research after paying a lot of attention to the concepts mentioned on the lectures slides and in the lectures really paid off. I stuffed up a question on synchronization, and my hope is that all who read this and take this subject will come back to this review before their exam and remember: 'while(a==b);' is a loop that will spin around doing nothing until a is NOT equal to b, not the other way around!

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#### silverpixeli

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##### Re: University of Melbourne - Subject Reviews & Ratings
« Reply #590 on: July 07, 2016, 12:17:16 pm »
+2
Subject Code/Name: SWEN30006 Software Modelling and Design

This subject is core to the Computing and Software Systems Major of the BSci. It runs in semester 1 and 2 but is a prerequisite for COMP30022 IT Project. The Informatics diploma also gives you the choice between this and I think INFO30005 Web Information Technologies with Mitchell Harrop. Additionally, computing and software engineering masters students may have to take this subject early on in their degree if they didn't complete it in their undergrad.

This subject has been around for a while, but this semester saw the introduction of a few major structural changes. A new lecturer, a new textbook (prescribed and followed quite closely) and a new programming language (Java, instead of Ruby + Rails) were introduced. All of the lectures, workshops and projects were designed from the ground up in light of these changes. So much so that any old resources probably don't apply.

I think overall after these changes the subject has come on a little too strong, setting the bar too high for the time commitment expected from students. The projects crossed the line in intensity, and even the workshops were taxing (depending on your tutor). Hopefully the balance will be improved in future iterations. In addition, the new prescribed textbook was a complete mess and I am amazed that people in the industry look to it as a good example of an introduction to this stuff.

However, SWEN30006 scores a point for its top quality lecturer Philip Dart, who really shone through as a beacon of clarity in such an inherently vague subject. Also, the exam could have been so terrible, but it was fair and well-balanced, and so it gets a point for that!

Finally, after everything they put us through, I do feel like I came out the other side knowing a whole lot more about Software Modelling and Design (which wasn't even completely uninteresting, just too intense), and so I guess it can have one last point for enabling / forcing me to improve my modelling and design skills!

Two 1h lectures
A compulsory 2h workshop
A significant amount of contribution to group projects (most of the second half of semester)

Assessment:
5% - Project Part A, individual
10% - Project Part B, group of 3
15% - Project Part C, same group of 3
10% - 10 x 1% for workshop exercise completion (or attendance, depending on your tutor)
60% - 2h exam in exam period

I'll describe the projects in more detail in the comments section

Lecture Capture: Y

Past exams available:
Since the dramatic changes for 2016, past exam problems were mostly unrelated. However, a practice exam was made available with solutions during the exam period, and in the final lecture the actual exam was previewed (without any of the text - just the mark breakdown / structure). Additional past exams should be made available for future semesters.

Textbook Recommendation:

The prescribed textbook Applying UML and Patterns by Craig Larman was followed quite closely, and most chapters were covered, to varying depths. I wasn't particularly fond of this text, but more about that later! Hopefully the 'content' section of this guide gives you a rough first overview of the course, and you can have fun piecing the structure together yourself when you study it.

UML (2.5 or something) is used extensively in the subject, and the short-but-sweet UML Distilled by Martin Fowler served as some nice light reading in the exam period. I imagine reading it earlier would have been a good idea too, haha!

Lecturer:

Philip Dart was the course coordinator and lecturer. He's a very articulate presented and experienced designer. I heard some people found his lectures boring but I think he's exactly the kind of person you want in charge of such inherently subjective content.

Additionally, Mat Blair played a substantial role as head tutor. However, this was his last semester at the university as he's off to work for Google (which suits me just fine; not a Mat Blair fan myself ).

Year & Semester of completion: 2016 Semester 1

Rating: 3/5

Your Mark/Grade: 94 (legit my 5th 94 so far wtf) EDIT: 95 (they forgot to include part of the grade )

The rest of this review goes into a LOT of detail about the classes, content and assessment structure as it was in 2016 semester 1, plus occasional advice for how to approach the subject. I hope that it helps you prepare for this subject if you decide to take it.

Content

So this subject was about learning to model real world domains, and, based on those models, design object-oriented systems. There was also a small focus on implementation of these designs. Underlying these topics was a discussion of a development process, in this case 'iterative and evolutionary development'.

The basic tasks of domain modelling are analysis of requirements and building of a domain model. We discussed Use Cases and UML Use Case Diagrams, Operation Contracts, System Sequence Diagrams (UML Sequence Diagrams that emphasise interactions at the border of the system), Domain Class Diagrams (UML Class Diagrams that present the concepts and relationships in that really exist in the domain being modelled, with no added software concepts) and UML State Machine Diagrams.

Object-oriented design is all about taking inspiration from a domain model to construct a system of objects who collaborate to fulfil requirements. The hard part is making decisions about which classes of objects to assign particular responsibilities, and this subject covers some general principles for making those decisions (see: GRASP). In addition, a small number of GoF Design Patterns (established solutions to common design problems) are studied. Designs are expressed using UML Class Diagrams, UML Sequence Diagrams, UML Communication Diagrams, and UML Component Diagrams (for emphasising large scale, architectural design decisions).

A design must be implementable, and implementation plays a small role in this subject. It's not really a focus of lectures, as Java was studied in prerequisite subject SWEN20003 Object-Oriented Software Development, but it was required for completion of some workshops and all of the projects.

All of this is introduced in the context of 'iterative and evolutionary development', which can be summarised in the following lessons/ideas:
- don't do UML (or any) modelling for the purposes of 'documenting', do it only as much as it aids understanding between clients and developers, or helps to handle a tricky design challenge
- embrace changing requirements as inevitable, and don't attempt to complete requirements analysis before designing, or design before implementing (or implementation before testing) like the so-called 'waterfall' methodology
- instead, control change by tackling high-risk requirements early, developing 'by feature' rather than 'by development stage' so that you can quickly incorporate feedback from clients and testing into subsequent features
We heard much more about iterative and evolutionary development in the subject because the textbook doesn't shut up about it! It literally floods every chapter with anti-waterfall-pro-iterative propaganda to the point where the few really good lessons on design are completely obscured by a steaming sea of shit. I wish anyone taking the subject enough patiece to find these hidden gems. Chapter 18 (3rd edition - it's chapter 17 in 2nd edition) is one of the less clouded examples; it contains explicit details about using GRASP Principles and would be a huge help for the second two projects.

Anyway, these four broad topics are the focus of the prescribed textbook, and lectures follow the textbook content closely. I think these topics were pretty well presented individually, however the textbook (and consequently the lectures, to a certain extent) was all over the place in terms of the order in which it presented ideas -- Larman unfortunately adopts an 'iterative and evolutionary' approach to teaching modelling and design, and the result is concepts introduced in early chapters but not completed until distant, unrelated sections.

Moreover, the lectures relating to modelling and requirements analysis were sprinkled throughout the lecture series, and likewise for object-oriented design and for iterative and evolutionary development topics. For these reasons, it really takes the whole semester to be able to look back and see where different ideas fit in. I know it would have been possible to provide a more helpful outline in the beginning, and approach the topics in a logical progression, even if the textbook jumps all over the place. Hopefully this will improve in future semesters based on student feedback.

Workshops

The workshops mainly consisted of collaborative design exercises, spanning the different types of UML diagrams we were studying across the semester. Basically we'd sit down and work on the exercises while the tutor walked around the room discussing people's solutions one at a time. Unfortunately, this didn't seem to be the most efficient structure, as it meant that you were quite clueless about your progress through the exercises until the tutor got to your area of the room. Most of the time, I got more out of overhearing the tutor's discussions with other groups than I was able to get by the time they got to us.

I guess this means the workshop experience was strongly influenced by the tutor's opinions on certain design issues. Well, since the tutor had so little time to talk with each group, it also would have been really helpful to be part of a stronger group of students, to trade ideas and work together.

The decision to assign a 1% grade for participation in each workshops was meant to incentivise attendance and engagement. However while some students from other workshops were awarded these marks based on active participation for the entire two-hour sessions, my tutor was quite strict, demanding every exercise be completed by the following week if we wanted to get the mark.

I'm sure lots of subjects have attendance and participation grades, however much I might despise them as a student who lives an arduous PTV journey away from campus. But given that it was pretty much impossible to complete each week's tasks in the allocated time, this was effectively homework, and it made me feel like I was back in year 11. I'd hope this requirement is relaxed in future semesters.

Projects

There were three projects throughout the semester,

Project Part A (5%); an individual project that was essentially a Java revision exercise, but involved working within a large and complicated provided framework.

Project Part B (10%); the first of the group assignments. In groups of 3, we had to take an existing (poor) design for a train network simulation and make improvements (justifying these in terms of the principles we were learning). We also had to implement these changes and submit an improved simulation system, with a few additional features.

Project Part C (15%); in our groups, we had to design and implement (from scratch) one of three different subsystems (part of a simple self-driving car simulation system in our case). In addition, we had to team up with groups that had worked on the remaining subsystems and put together a working self-driving car system and write a short report about how that went.
The submission for this final assignment had three parts:
- (1/15) design draft submission (free mark for submitting a complete draft)
- (9/15) final design submission
- (5/15) implementation and report submission

The idea was that we'd submit a first copy of our design two weeks after the specification was released, get feedback in the workshop the following week, and then make any adjustments necessary to improve the design from there before making the final design submission at the end of that week. At that point there were then two weeks to finish implementation and integration with other teams.

Unfortunately, this year, the projects were very intense in terms of what was required just to complete them on time. I think even the tutoring team felt the pressure, for example for Project C there were meant to be additional self-driving car maps and subsystem testing tools released so that we didn't need to rely too much on other groups to make sure our subsystems worked, but these were never released.

There were a few other little issues, such as the choices of subsystems not being finalised until about a week after the spec was released, leaving only a single week to create the initial design. Also, the feedback workshop was really rushed (and I can't imagine what it would have been like for students who had their workshops late in the week, leaving only a few days until the Sunday night final submission deadline).

According to the tutors, the vast majority of students were destroyed by even the first project, which wasn't intended to be challenging at all (and it wasn't, for someone who was on top of the prerequisite skills). The final project was apparently going to be a lot more complicated but ended up getting scaled back due to the cohort's performance in this first project. I think it still crossed the line, and even with a strong team we struggled to get something together with the incredibly constrained time line. We ended up with great marks (1.45/15, with 5/5 for implementation, after marks eventually came out), so perhaps this difficulty was taken into account.

Exam

On a lighter note, the exam was pretty much the greatest thing that it could have been. There was a lot of fear surrounding the idea of an intense modelling exam that was going to require some very fast project-scale design work (not really suited for a two hour exam) because of the practice paper.

These fears turned out not being realised, and the actual exam was very well balanced overall. It was mostly made up of smaller modelling exercises and also questions that emphasised evaluating a model rather than constructing one from scratch. There were also questions that involved factual recall from random parts of the lecture notes / textbook, so re-watching the lectures and re-reading the text over the few days leading up to the exam turned out to be rewarded.

The expectations behind each question were quite clear, and this was really appreciated in a subject where at times it can seem like there's no clear correct response. Everyone that I spoke with afterwards said they didn't quite finish, and I only just managed to get everything down in time, so perhaps it was a little long, but overall I don't think I could have asked for a better exam than the one we got!

« Last Edit: July 13, 2016, 08:43:51 pm by silverpixeli »
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#### kingfisher

• Posts: 14
• Respect: +1
• School: Unimelb
##### Re: University of Melbourne - Subject Reviews & Ratings
« Reply #591 on: July 07, 2016, 08:04:32 pm »
+4
Subject Code/Name: DASC20011: Companion Animal Biology

Workload: x2 2 hour lectures per week

Assessment: Written assignment (50%), end of semester exam (50%)

Lectopia Enabled: Yes

Past Exams Available: Yes (via library)

Textbook Recommendation: None

Lecturers: Ian Bland, Peter Cakebread, Sally Haynes, Sonja Needs

Year & Semester of Completion: Semester 1 2016

Rating: 5/5

Comments: This subject has been one of the most enjoyable and relevant subjects I have taken to date. While there is a biological focus, there also seemed to be plenty of people taking this as breadth so it’s not too ‘sciency’ and covers more topics is less depth. Topics covered include general healthcare of companion animals, feeding, behaviour, genetics and ethics. You also look at how to care for more exotic pets like reptiles and fish. I thought all the topics were really interesting and anyone with pets should get a lot of usable info out of them.

The assessment was really fair. It involved an initial assignment on a specific topic (animal product/healthcare plan/other topics I can’t remember) that was worth 30%. The remaining 20% comes from a summary of your assignment (almost a poster format) and then peer assessment of these summaries. The peer assessment is really well done- all the summaries are uploaded (anonymously) and you’re given 5 summaries to rank from best to worst including your own. You’re then marked on how accurate you are with these rankings. You’re given stacks of time to do all these and they’re weeks apart so they’re not too stressful, and because you can choose your own topic it should also be interesting and fairly easy to do well in. The exam was also very fair and pretty similar to past exams, so there weren’t really any surprises.

There are also legit puppies and ponies towards the end of the course, and I would recommend taking the subject for this alone. A lovely bunch of owners bring their dogs into the System Gardens and Sonia runs a workshop in dog training (mostly scent training) which is heaps of fun!! And we also go on an excursion to assess the weight of some horses, which was really nice as well.
Overall, if you have any interest in animals this is a great subject to take. If you’re interested in animal science (and are subsequently taking other DASC/animal units) then I found there was a fair bit of overlap, but definitely not enough to feel like it wasn’t worth doing. If you’re looking for an interesting and reasonably relaxed breadth then I would definitely consider this subject.
« Last Edit: July 13, 2016, 10:49:14 am by literally lauren »

#### cassiecate

• Victorian
• Forum Regular
• Posts: 72
• Respect: +9
##### Re: University of Melbourne - Subject Reviews & Ratings
« Reply #592 on: July 08, 2016, 05:36:37 pm »
+6
Subject Code/Name:  MAST20026 Real Analysis

Workload:  3 x one hour lectures per week; 2 x one hour practice classes per week.

Assessment:  6 written assignments due every fortnight during semester contributed to total 20%, a 3-hour written examination (80%).

Lectopia Enabled:  Yes, with screen capture.

Past exams available:  Yes, 7 past exams from 12 to 15, some with solutions.

Textbook Recommendation:  Lecture notes and problem booklet can be purchased as a bundle from co-op (10-20 bucks if I remember correctly), there's also a pdf version on lms.

Lecturer(s):  A/Professor Deborah King

Year & Semester of completion:  2016, Semester 1

Rating:  4.5 Out of 5

Coming from Calculus 2 and Linear Algebra, Real Analysis may be the most students' first brush with pure math. Unlike first year subjects and Vector Calculus which mostly concentrated on how to solve problems/calculations, it focused more on "Why" i.e. proofs behind basic mathematical theorems & precise definitions & axioms.

Lectures

The subject starts with introducing mathematical symbols, logic operations and common quantifiers, as well as how to construct truth tables. Then we'll learn the most important part of this subject (imo), techniques of conducting proof/disproof, there are several ways, like axiomatic/direct/contrapositive/contradiction proof and proof by induction/cases. Deborah will go through each meticulously and demonstrate on a few examples, there are also a lot more similar questions on problem booklet if you're looking for more practice. After this we will methodically learn about bounds and sequences, then how to prove sequence convergence/divergence by epsilon-M definition, sequence limits and some special sequences and their proof (mainly Cauchy). Then we move to function which is basically a more "general" version of sequence (from R to R instead of from N to R), after introducing the knowledge about (deleted) neighbourhood, we again learn about function convergence and limits, then we'll goes to continuity and differentiability, learning both definitions and theorems like intermediate value theorem, mean value theorem and Rolle's theorem. Next topic is Riemann integration, Deborah will starts with Riemann sums, then refinements and finally Riemann integrable & improper integrals (all with plenty examples). The last part of this subject is series, we'll again learn about how to determine series convergence by appropriate tests, much like in Calculus 2 and we will specifically learn about power series and thus extends to using Taylor series/polynomial to approximate functions, then Fourier series as well, though we won't go too deep into these last two. There will be plenty of definitions and theorems in this subject and all of them are very important and will be examined in assignments and/or final exam, so please make sure you know them and know them well.

Deborah is a very responsible lecturer, always prepared well for class, explained definitions/theorems pretty clear and she's very keen to listen and answer questions from students both in lecture and afterwards. She also puts out a youtube channel for solving problems that were left in lectures. Though she tends to speak really fast and write excessive amount of notes at the same time (amazing ability tbh), so it's possible you cannot keep up with all the contents in class and have to watch recordings afterwards for a better understanding. And it's also normal for the lecture to go overti\me (usually 5 mins or more) so prepare for this if you got a back-to-back class at the other end of the campus.

Practice Classes/Tutorials

There are two tutorials each week which are not streamed, one on Monday/Tuesday, the other on Thursday/Friday with different tutors. Each is typical math tute, three or four people form a group and solve a question sheet on whiteboard, tutor walks between each group and answer questions. The first tutorial will focus on more theoretical side of last week's contents, while the second one will focus on practical questions of this week's contents. Since this semester's lectures were on Tue & Wed & Thur, there would be almost not enough time to watch the lectures before you went to the second tutorial if you've missed class. So better not leave a week's lectures to the weekend unless you're planning on showing up to the tutorials knowing nothing. Some tutors will also do pop quizzes about definitions before the actual tutorial starts. The solution for both tutorials' worksheets will be given at the end of the second tutorial, and the second tutor will also be the one who marks your assignments.

Assessments

The six fortnightly assignments are not particularly difficult, they are rather focused on checking if you could write a rigorous mathematical proof with all the necessary reasonings and justifications and correct two-column format, just put some time in it and check really carefully. The last assignment has an essay question which will reviewed by Deborah and it's actually interesting, let you think about how your understanding about math change through this semester.

There's really not much to say about the final exam, it has quite a routine style, much like the exams from previous years, there will be one or two tricky sub-questions but the most parts's difficulty is consistent with tutorial & exercise questions. Make sure you go through all the notes and definitions in SWOTVAC as there will be a few questions ask for writing down precise definitions and theorems which should be easy marks.

Overall I think it's a really nice second year subject to try even if you are not intending on major in math (as for math majors this is almost a must-have subject no matter which specialization).  I must admit I've always considered intelligence is the most significant factor in doing math and underestimated the importance of hard-working and commitment, after this semester I wouldn't ever say so. Like Deborah said in class, this subject has the highest passing rate in all second year math subjects, if you put in effort you'll definitely get a rather satisfying result.
2015-2018: Bachelor of Science (Mathematics and Statistics) @ UoM
Concurrent Diploma in German @ UoM

#### stolenclay

• Victorian
• Trendsetter
• Posts: 133
• AN, pls.
• Respect: +76
##### Re: University of Melbourne - Subject Reviews & Ratings
« Reply #593 on: July 10, 2016, 06:02:29 pm »
+9
Subject code/name: MAST30020 Probability for Inference

Workload: Weekly: 3 x 1-hour lectures, 1 x 1-hour problem-solving class

Assessment:
 10 individual assignments 20% 3-hour end-of-semester exam 80%

Lectopia enabled: Yes, with screen capture.

Past exams available: In 2016 Semester 1, two past exams were available with solutions. More were available on the library website without solutions.

Textbook recommendation: Karr, A. F. (1993). Probability. New York, US: Springer-Verlag New York.

The lecture slides and problem sheets contain references to this book. It is available as a digital copy for free as long as you are a student at the university, though it is not really needed; the lecture notes are self-sufficient.

Lecturer(s): Professor Konstantin (Kostya) Borovkov

Year and semester of completion: 2016 Semester 1

Rating: 4.9 out of 5 (10 assignments kill the student)

Comments: (NB: I did Accelerated Mathematics 2 instead of Calculus 2 and Real Analysis, but I think most of what I say about AM2 is still relevant to Calculus 2 and Real Analysis. I also did Probability instead of Probability for Statistics, but that also shouldn't make much of a difference.)

MAST30020 Probability for Inference will open your eyes to how hard undergraduate studies in probability can be.

This is a demanding subject in every possible way. The theory is dense and the workload immense. Neither breadth nor depth are sacrificed in the delivery of this subject. Probability for Inference is a rigorous treatment of probability theory within the limitations of an undergraduate course, and it approaches the field from the perspective of measure theory, which is an area only taught at a graduate level at this university. There is reasonable discussion on an unexpectedly wide variety of aspects, even though a perfect understanding would require tools beyond those of an undergraduate.

Fortunately you have the perfect lecturer for this subject: Kostya (which he is called instead of Konstantin) is a lively and humorous lecturer who is able to balance the very rigorous topics with accessible explanations. He is always prepared to help the students who help themselves. That is, he will not mollycoddle you, but he is certainly very willing to guide and prompt you, and I find that approach to be optimal in this subject.

In many respects, Probability for Inference resembles MAST10009 Accelerated Mathematics 2. Both reconstruct an area of mathematics of which you have a rudimentary knowledge prior to taking the subject. AM2 guides you through the rigour that was sorely missing in your high school calculus studies, while Probability for Inference revisits the content in MAST20004 Probability far more meticulously. Indeed, just like AM2, you will probably struggle heavily for the earlier parts of the course, and it is these earlier topics that will support your understanding and star in some vital arguments for other problems and topics occurring later in the semester.

This subject is an elective for the Statistics and Stochastic Processes specialisation of the Mathematics and Statistics major in the Bachelor of Science. Naturally, most of the cohort for Probability for Inference are students intending to specialise in Statistics and Stochastic Processes. However, you will quickly find that many of the students are those from other specialisations who have returned for a second taste of probability after a pleasant experience in MAST20004 Probability or MAST20006 Probability for Statistics. There are also some Master of Science students taking this for their secondary area of mathematical study.

As fickle students you are probably aware that subject choices often come down to the quality of the lecturer (or, more precisely, a vicarious judgement thereof). The rather high enrolment in this third-year subject (59 students in 2016 Semester 1) despite its notoriety for being so difficult and its status as merely an elective is only a testament to just how fantastic of a lecturer Kostya is.

Subject content
Probability for Inference ties together many of the loose threads in Probability and Statistics while introducing some new tools and techniques. Overall I would label Probability for Inference as a subject in both probability theory and mathematical statistics (mathematical statistics referring to the mechanics behind various statistical tools and frameworks). The course (as well as Kostya himself) places heavy emphasis on rigour and proof, and the content is heavily abstract and conceptual but is delivered exceptionally well in an accessible manner by Kostya.

Probability for Inference begins with the same few definitions that you probably glossed over at the beginning of Probability. However, it introduces the concept of sigma-algebras, which may have been tersely mentioned in Probability as the set of events which are "nice". One of the most fascinating aspects of sigma-algebras to me is that it can be seen as the mathematical manifestation of information or, more strictly, information "potential" from observations of a random quantities. This particular way of viewing information as a sigma-algebra is precisely the motivation behind the use of martingales in higher level financial mathematics if you study any (ACTL40004 Advanced Financial Mathematics I and beyond, although to some extent ACTL30005 Models for Insurance and Finance also).

A few properties of probabilities (i.e. the function that assigns fractional values to events, often with the familiar notation P) are discussed. The probability axioms are, of course, part of this. Most of the other properties relate to sequences of events, which is something you will not have seen in Probability. Following this are the familiar faces of distribution functions, probability mass functions, and density functions, but they are of course introduced in the context of our newer framework.

Random variables and expectations are the next point of discussion. Again these are rebuilt from a more rigorous perspective than in Probability, and again some unfamiliar properties of and results regarding expectation are discussed (yes, there is more to expectation than just linearity). The concept of conditional expectation is the next topic; it is probably the first hurdle in this subject if you have found the content manageable so far, as conditional expectation is no longer the tame computational beast that it may have been in Probability. In my opinion, conceptualising expectations and conditional expectations as the "best guess" of some random quantity (possibly given some information beforehand for conditional expectations) is the way to navigate through this topic and further uses of expectation. In fact, thinking about conditional expectations in this way corresponds very naturally to the Bayesian estimator under the quadratic loss function, which you may recall from MAST20005 Statistics.

With so much content falling under the field of probability theory, you may doubt the relevance of "Inference" in this subject's name. Fear not, for the weeks you have spent learning mind-numbing probability theory is about to find some use in statistics right within this subject! The discussion on statistics in this subject takes place in two parts, with the interlude dedicated to two areas of probability theory, one of which is the unequivocal cornerstone of (frequentist) statistics, and the other of which is indispensable in the study of further probability theory.

The first part of discussion on statistics covers and extends some of the theoretical topics encountered in MAST20005 Statistics: maximum likelihood estimation and sufficiency. The prominent theorems in this section are none other than the Neyman–Fisher factorisation theorem and the Rao–Blackwell theorem. This section is entirely taught from first principles, as Statistics is not actually a prerequisite for Probability for Inference. I enjoyed the treatment of these two topics far better than I did in Statistics, although that likely comes down to a personal appreciation for more theoretical discussion.

The two sections of probability theory that follow this opening discussion on statistics are limit theorems and characteristic functions. From Probability you should already be familiar with the law of large numbers and the central limit theorem; these are the main limit theorems, and in this section the mechanics behind these two theorems and other related phenomena will be examined. There is a slight resemblance to limits as taught in AM2, in that you should be prepared to maintain an epsilon–N-level of rigour in your solutions.

The section on characteristic functions was, to me, the most eye-opening of this subject. Characteristic functions may have been mentioned in passing in Probability, around the time that moment-generating functions and probability-generating functions were introduced. Characteristic functions retain many properties of moment-generating functions (uniqueness, can be used to circumvent convolution integrals, can be used to compute moments through differentiation), but are (subjectively) better. One of the ways in which it is superior is that the characteristic function of a random variable is always well-defined; the same cannot be said for moment-generating functions. The characteristic function of a random variable is the Fourier transform of its density (with respect to an appropriate measure), and indeed a perfect inspection of some of its properties will mandate some results from complex analysis; however, the lectures will be manageable without having studied complex analysis. As a consequence, the density function and the characteristic function of a random variable (or rather, its distribution) are intimately connected. I gather that many of the properties discussed in this subject likely follow from corresponding results in Fourier analysis; in any case, the expectations of the cohort for characteristic functions will not require experience with complex analysis.

Characteristic functions are mainly used to revisit and establish some of the limit theorems. This is done with the assistance of Taylor polynomials. In Probability for Inference, you must be[come] very comfortable with single-variable Taylor polynomials (and be willing to accept that Taylor's theorem holds in the complex case if you have not studied complex analysis). In particular, whereas in AM2 you may have used Taylor's theorem with Lagrange's form of the remainder, in Probability for Inference, the use of Taylor's theorem is accompanied by Peano's form of the remainder, which simply uses Landau's Little-O notation to express the remainder term in Taylor expansion. For the purposes of this subject, Peano's form is probably more concise and suitable than Lagrange's form is.

The return to statistics is signified by an excursion into the validity of the chi-squared goodness-of-fit test. In Statistics, it is not immediately clear how the claimed null distribution of the test statistic is a valid approximation. With the results on limit theorems and characteristic functions, you are now able to conclusively establish the rationale behind the null distribution used in this goodness-of-fit test.

The subject concludes with the discussion of empirical distribution functions and asymptotic behaviour of maximum likelihood estimators. The discussion on empirical distribution functions culminates in the fantastic Kolmogorov–Smirnov goodness-of-fit test, and like the chi-squared goodness-of-fit test, the null distribution is derived rigorously (with the quotation of some intermediate results which would probably take too much time to discuss). The discussion on maximum likelihood estimators shows how they are asymptotically normal and unbiased and establishes the relevance of Fisher information (remember the Rao–Cramér lower bound from Statistics?) in the mean-squared error (variance).

Lectures
From the very beginning of the subject (actually, even before), the lecture slides for the entire subject are available online. They can be found on the LMS or on a page where Kostya makes available to the public the main resources in the subject (the link is http://www.ms.unimelb.edu.au/~s620323/). The set of slides is an excellent resource, and of course Kostya's lectures follow the slides perfectly (but he will add a bit more). There are usually some references to problems on problem sheets, so Kostya will update the slides every now and then if the problem sheets have changed since the last iteration of the subject.

Kostya delivers his lectures with the document camera switched on, and in the Russell Love Theatre (where most third-year maths subjects are held), the document camera occupies one of the projector screens, while the current slide occupies the other. In my semester of completion, the lecture recording consisted only of the activity on the document camera, so you would not be able to see what slide Kostya was currently discussing in the lecture recording.

The lectures are interactive, entertaining, and of course very educational. Kostya delivers lectures in his characteristic exuberant manner without sacrificing the care needed in rigorous arguments. As I have mentioned, I found that Kostya has the uncanny ability of translating the "burly" and intangible rigour of probability theory into very accessible intuitive arguments. Of course, what is intuition to one can easily be an absolute mystery to another; some of these pieces of intuition are not completely obvious, so to say, but with experience from lower level maths subjects (and particularly the variety of mathematical problems therein), what Kostya delivers as intuition should be mostly regarded as such by the cohort. For example, geometric properties of projections and convex sets are mentioned throughout the discussion of conditional expectations. This is perhaps not the best example of intuition (being a consequence of considering the set of random variables with 0 mean as a Hilbert space), but it highlights Kostya's resourcefulness in using analogies from other areas of mathematics to which most students will have had exposure. Another example is Kostya's explanation of Lebesgue integrals, which he summarises as partitioning the integrand by range rather than by domain as in the Riemann integral (with a strange example of counting money spread on the floor).

Kostya is always ready to ask the audience questions: some just to see if knowledge in the recent few lectures has been retained; others a prologue into the topic of discussion for the day; and occasionally a "Can I put this on the exam?" to make us ask ourselves whether we really know the content. Kostya's questions almost created an atmosphere of discussion, which I feel in the university study of mathematics is very necessary. Of course, the "discussion" was usually dominated by Kostya, but his questions were rarely unanswered, and the interaction between student(s) and teacher in the lecture hall created a sense of engagement which I have rarely found in a university subject.

The actual structure of a lecture naturally varies according to what's on the lecture slides. A lecture could contain
• an explanation of a difficult proof;
• outlines of proofs when they are beyond an undergraduate student;
• explanations of multiple smaller proofs (particularly when exploring properties); or
• intuition for or demonstrations of the more abstract concepts.
None of these are particularly surprising in a maths subject, but it is of course the higher proportion of proofs in this subject which gives Probability for Inference its overall theoretical orientation. Now, Kostya's (unspoken) expectation is that any proof which is given completely in lectures (i.e. not those which are clearly stated as beyond the undergraduate student) is fair game in an exam, and it is rather daunting that this refers to probably half of the slides. Kostya's aim is certainly not to encourage rote-learning. In fact, Kostya encourages the cohort to form the good habit of retaining the key ideas of a mathematical proof, which, when combined with the mathematical tools at hand, are sufficient in reproducing the proof. I would strongly recommend highlighting and remembering the key ideas or techniques in all the proofs in the lecture slides. It develops your mathematical maturity and is also quite fulfilling when you realise that you are able to reproduce proofs without further assistance by just noting these key ideas. Of course, it is even more fulfilling to find these key ideas yourself; unfortunately that is rather difficult and thankfully not an expectation.

For a few weeks during the semester, Kostya also conducted in-class quizzes (not contributing to the final grade). This was done on the Socrative web platform, and students took part using their mobile phones. The questions were all true–false or multiple-choice questions and generally tested knowledge in the last few lectures or so. This was opt-in, but there was nothing to lose since the performance did not contribute to the final grade, so it was a good revision tool to check your understanding of the recent lectures. Not all the questions were as straightforward as you would expect of multiple questions, especially since there was an unofficial time constraint of however much time Kostya decided was necessary. Most questions seemed to set up some random variables and ask if certain statements regarding the random variables were true, which ranges from simple to quite puzzling given the scope of Probability for Inference.

In the final week, if there is time Kostya will spend some lectures doing a past exam. In my semester of completion the discussed exam was not a past exam to which solutions were available online (that would have been slightly redundant), so it is ideal to be present for these lectures.

Problem-solving classes
When you look at the university timetable entries for Probability for Inference, one of the most striking things is that there is only one time slot for the practical class. Unlike practical classes in other maths subjects, in Probability for Inference, these resemble lectures more than they do tutorials. In fact, they take place in the same place as the lectures (at least this was the case in my semester of completion).

Kostya calls these classes "problem-solving classes", and the entire class will consist of Kostya solving problems on the weekly problem sheet, which Kostya will print and bring to the classes as well as post online. These problems are not straightforward; even though Kostya readily encourages students to present solutions in problem-solving classes, there is hardly ever any student brave enough to do so. Even so, Kostya maintains interaction with the cohort as he does in lectures. Some of the problems in these classes are simple applications of the theory learnt in the lectures in the week before. However, by and large these problems require new techniques or arguments not seen in lectures. Kostya will also sometimes offer extra insight into the theory during these problem-solving classes, although this is the natural thing to do when completing problems which require new methods.

I think the benefit of attending problem-solving classes is clear. Any passionate student should want to see how the content in lectures can be used or extended in various problems. I think it is fair for problems resembling those on problem sheets to appear on exams, so you assume some risk by missing these classes (they are not recorded like the lectures). Kostya will also tell you that it should not be surprising if the exam contains similar questions; I do not recall that happening in my end-of-semester exam, however, so perhaps he was feeling generous in my semester of completion.

Problems listed on problem sheets are quite often referenced in lecture slides, and this creates a strong sense of coherence between the material in problem-solving classes and lectures. Often the situation will be that the significance of a certain problem on a problem sheet is highlighted in a later lecture (usually in the form of some small phenomenon). This reserves time in lectures for the more important aspects, but ensures students have a robust knowledge of everything that is happening.

It is regrettable that there is often not enough time for Kostya to go through all the problems on the problem sheet. Kostya often resorts to skipping computational steps or claiming some steps are obvious in order to save time; he will more readily claim that something is obvious in these problem-solving classes than in lectures. Solutions are posted online after the class, but I still personally believe greater value is gained from hearing Kostya's explanations for some of the more difficult problems rather than reading solutions on paper. Nevertheless, for the problems not covered in the problem-solving class, it is your responsibility to be familiar with the solutions posted online.

Assignments
This is quite possibly the single aspect of Probability for Inference that will leave students with somewhat bitter memories.

You have ten assignments for this subject in total. In 2016 Semester 1, each was due at 5pm on Mondays from Weeks 3 to 12. These are all standard-length maths assignments — the length of these assignments does not compensate in any way for how many there are (the length of those in MAST20004 Probability are a good indication). This is simply an enormous time commitment for a single subject, and while I think the assignment workload is somewhat warranted due to the difficult theoretical nature of the subject, for me, ten assignments still falls on the extreme side.

The assignments problems are on the same sheet as the problem sheet (usually on the next page), and they are of a similar difficulty. The trouble is that to do well on the assignments requires (in my opinion) an excruciating amount of effort, not to mention how many of them there are to begin with. Kostya expects the rigour and detail which he himself displays in lectures, and for a first exposure to rigorous probability theory, sometimes it can be difficult to identify the areas that necessitate more rigour. The level of detail Kostya presents in problem-solving classes is a bad indication of what is expected of you; as I have said, Kostya is under time constraints during those problem-solving classes. However, a good indication, outside of the lecture slides, is probably the solutions to the problem sheets which Kostya posts online. One example of the level of detail required is that Kostya expects "by linearity" to be written somewhere when you use the linearity property of expectation or conditional expectation.

The scoring system for assignments is as follows: For each assignment, Kostya (or someone to whom he has delegated the marking) will select a question to mark for the entire cohort. This gives a mark for each assignment (or really, just the respective question selected for the assignment) that is usually out of 5 marks (but sometimes more). The average percentage over all ten assignments (equal weighting among all ten) then receives a 20% weighting in the calculation of the final grade, with the percentage on the exam receiving the remaining 80% weighting.

This also means that a mark on one assignment may have more effect on your final grade than that on another assignment (very marginally), but you will not know which assignments these are, as you are not told beforehand the maximum mark of any assignment. For example, if there were 3 assignments marked out of 2, 10, and 50 (just an example — the maximum marks are more consistent in reality), and your scores were 1, 10, and 50 respectively, then the percentages earned on your assignments would be 50%, 100%, and 100% respectively, and your average percentage would be 83% (rounded down). Notice that if the single mark you had lost was on the third assignment rather than the first, your average percentage would have been 99% instead (rounded down).

Kostya published assignments marks twice throughout the semester: once after the fifth assignment, and once after all ten assignments. Students were listed by student number (no names). For brevity, here were the summary statistics after all ten assignments in my semester of completion, the data in consideration being the the average percentages multiplied by 20. (The minimum of 0 is not a mistake.)
 Min. 1st Qu. Median Mean 3rd Qu. Max. 0.00 10.29 12.65 12.33 16.23 19.20

As mentioned, the questions on assignments are of similar difficulty to those on the problem sheets, though probably slightly easier. The questions are usually either computations or applications of the theory to prove some properties. Not all assignment questions are straightforward; some will require some thinking as to the optimal method of approach, although for the computational questions this is usually not the case. For some questions Kostya will provide hints; sometimes I found these somewhat unnecessary or a slight giveaway, but other times they offered the right amount of guidance.

As it was for problems on the problem sheet, the lectures slides will sometimes reference problems on the assignments. The reverse also happens; sometimes an assignment question will be the investigation of a property that was merely quoted but not established fully in lectures. All in all, the assignments and problem sheets are very coherent aspects of the subject which aim to give a more holistic understanding of probability theory.

End-of-semester exam
This is a 3-hour exam that is probably on the long side due to the nature of the content in Probability for Inference. No cheat sheet is allowed. A scientific calculator is allowed but will not be of much use.

I think that the level of difficulty of the exam is very consistent with the subject as a whole. The exam is more computational than the assignments, but theoretical questions still have decent representation.

Kostya expects a high degree of familiarity with the lecture slides (and possibly more, but you will perform decently with just being familiar with the lecture slides, although that is no small task anyway). As mentioned earlier, the proofs on these lecture slides will almost surely (but not certainly!) make an appearance on the exam. I mean this literally; there will be subquestions which effectively amount to reproducing some portion of the slides. You would, however, be a fool to rote-learn the proofs on the slides. That is not recommended, optimal, nor, in my opinion, acceptable for a student of third-year mathematics. In 2016 Semester 1, there were also some subquestions which had featured almost identically on assignments.

I believe the way to approach exam preparation for such a theoretical subject is no different to the preparation for the general theoretical mathematical subject. Familiarity with all the results of key theorems and properties is absolutely essential, but the next step varies in difficulty from student to student. Perfect preparation for the exam will involve, more specifically, familiarity with the techniques used to establish the key theorems and properties, and also some of the content outside the lecture slides. It is not possible to form an exhaustive list of areas with which you should be familiar, as this is nevertheless a third-year mathematics subject, and creativity and critical thinking, as well a good memory, will underpin any level of success in the subject. For example, something as simple as the square of any real number being nonnegative is a well-known fact to most students, but would you be able to recognise its utility for a question asking you to provide a proof (of something else)? Would you be able to recognise that an expression was in the form of the Taylor series for the exponential function if it wasn't explicitly provided in that form? These are the kinds of questions that you need to ask yourself if you are aiming for the highest levels of achievement in this subject and further mathematical studies (particularly if they are heavy on theory).

In terms of topic coverage in the exam, elements of all topics will be present, although this is only to be expected if you have personally experienced the twelve weeks of teaching by Kostya in Probability for Inference. Due to this, the structure of exams seems to remain somewhat invariant, in that there will be generally be
• one or two questions on the probability (P) definition, axioms, and properties;
• one or two questions on the sigma-algebra definition and properties;
• a question requiring the plot of a distribution or density function as well as the computation of other quantities relevant to the distribution;
• questions involving computations with or properties of expectations and conditional expectations;
• questions involving sufficient statistics and maximum likelihood estimation; and
• questions involving the convergence modes and characteristic functions.
Again, this is not an exhaustive list. There are most likely other more specific topics which make appearances on the exam less frequently, such as empirical distribution functions or multivariate (normal) distributions. However, the above list should contain the topics common in all exams for Probability for Inference. There will generally be proof-style questions for most of the topics present on the exam, but I believe more of the exam is computations rather than proofs.

Also of note is a true–false question (with multiple subquestions). These are not pure true–false questions, however; you still need to provide justification for your answer. These questions resemble those in the in-class quizzes, and the justifications will be mostly very short. Why is there a true–false question on the exam? Kostya admits that they are easy to mark — very honest answer.

Kostya is hesitant to make available the solutions to too many past exams, as he prefers students to learn content rather than "learn exams" (i.e. prepare specifically for the sorts of questions on previous exams). In my semester of completion there were just two past exams with solutions provided. More past exams were available on the library website, but Kostya refused to provide solutions to those.

MAST30020 Probability for Inference is a well-administered and rewarding subject, but certainly not for the light-hearted. It is an excellent foundation to have for further studies in probability theory and (mathematical) statistics. If you are prepared to dedicate effort into this subject and you are interested in the intricate mechanics of probability theory, then I highly recommend this subject.

An interesting fact: Kostya's father was a student of the great Kolmogorov himself! I was also told that Kostya's father was in fact Kolmogorov's best student, although I was unable to verify that myself. In fact, Kostya publishes research with his father, who is by now somewhere in his eighties.
« Last Edit: July 18, 2016, 01:48:02 am by stolenclay »
Thoughts on my journey through university
2014–2016 BCom (Actl), DipMathSc @ UoM
2017–2018 Master of Science (Mathematics and Statistics) @ UoM

#### cassiecate

• Victorian
• Forum Regular
• Posts: 72
• Respect: +9
##### Re: University of Melbourne - Subject Reviews & Ratings
« Reply #594 on: July 11, 2016, 08:37:40 pm »
+6
Subject Code/Name:  MAST20004 Probability

Workload:  3 x one hour lectures per week, 1 x one hour practice class per week, and 1 x one hour computer laboratory class per week.

Assessment:  4 written assignments due every three week during semester each contributed to 5% (total 20%), a 3-hour written examination (80%).

Lectopia Enabled:  Yes, both streams with screen capture.

Past exams available:  Yes, 6 past exams from 09 to 15, all with solutions and all assignments from 14 & 15 with answers.

Textbook Recommendation:  Lecture notes can be purchased from co-op, there's also a pdf version on lms. Recommended textbook is Fundamentals of Probability with Stochastic Processes, 3rd Edition by S Ghahramani (Pearson Education, Inc. Upper Saddle River, NJ, 2005). There are a few copies at ERC, you could also find pdf version (with solutions) on google (if anyone need it feel free to pm me).

Lecturer(s):  Dr Nathan Ross for stream 1, Dr Mark Fackrell for stream 2.

Year & Semester of completion:  2016, Semester 1

Rating:  3.5 Out of 5

Probability and Statistics from semester 2 are compulsory for actuarial students, so there are fair share of commerce students in this subject, I was in Dr Fackrell's stream (stream 2) and rarely listened to Dr Ross's recording since they have rather different teaching & approaching styles imo. It's actually kinda interesting that some of my friends in stream 1 believed Dr Fackrell is better at teaching and friends in stream 2 thought exactly the opposite, so in the last few weeks of the semester there were quite a lot students choosing to go to the other stream.

The prerequisites for this subject are just Calculus 2 and Linear Algebra, but I think it needs at least some knowledge from vector calculus as well as real analysis. Have done vector calculus last semester and been doing real analysis concurrently this semester, I went through ok with all the proofs and definitions. However, my friend who did probability last year without any knowledge from these two had found herself having difficulties a lot of times.

Lectures

The first few weeks of this subject is quite relaxing, just like doing probability questions from high school. We also learnt about both discrete and continuous variables, their distribution function (cdf)  and probability mass/density function (pmf/pdf). Then coming into some special distributions like binomial, geometric, negative binomial, normal, exponential and gamma, a large proportion of class will be used in proving/deriving these distributions' pmf/pdf as well as their expected values and variances, which can seem boring at the time. There were far too less examples in this part and many students I know (and myself as well) find it quite hard to put theorem into application. Then we'll learn bivariate random variable and their joint/marginal/conditional pmf/pdf. We'll study the transformation i.e. functions of single random variable and bivariate random variable. One very important topic is about condition on RV and how to derive expected values, variance and probability under this circumstance, there will be a great proportion of final exam focusing on this and using any of the distributions that were previously learnt. Then we'll learn about probability generating function and moment generating function, which extending to the last topic of branching process.

The lecture is heavily based on proof like I mentioned before and can be really hard to keep up as Dr Fackrell usually went quickly between steps and rarely gave enough justification, I usually needed one more hour after each class to understand how each step of his proof worked. The order of topics is also a bit confusing at the time since Dr Fackrell actually taught each of them quite separately and never really engaged in explaining how to connect them, it's not until SWOTVAC when I was revising the whole subject and finally came up with an (not so clear) understanding of how each topic connects with each other.

Practice Classes & Computer Labs

The first hour of tutorial is just typical math tutorial where you do questions with a table of other students on whiteboard and the tutor hovering around to help you if you got stuck. The tutorial questions can be very good and much needed exercises for better understanding of all the definitions, since there aren't many examples given in the lectures. The second hour is joining by another tutorial and doing tasks related to lecture contents in a computer lab, there's no lab test in this subject so not many people pay a lot attention to the computer lab, but there will be a question in final exam entirely relied on contents from lab (though not a lot marks), this year it's the second last question with 6 marks out of 100.

Assignments

The first assignment is quite easy while the other three are much more difficult. There are at least 4 (usually 5 or 6) questions in each assignment, only two of them will be selected to mark. It's a good idea to redo the similar tutorial questions and discuss with your friend. From this year's feedback I think the relatively easy ones are more likely to be marked (or at least they will leave the most difficult one out). Hence despite the rather time-consuming process of doing the assignments (roughly two nights each for me and similar for my friends) most people seems to get good overall marks from them.

Exam

If we've been allowed to describe the exam in one word I believe a lot of people will choose HARD. I've never felt completely lost in a exam like this, at last I even started calculating if I could pass. The exam allowed you to bring in a double sided A4 cheat sheet and it had the similar difficulty level as the assignments with a few harder parts for last several questions. I think it's really aiming to test if you actually understand all the concepts taught in the semester instead of just knowing how to applying the method. So it's really important to know how each distribution behaves, how to determine which one to use and how to manipulate them under certain circumstances. Simply cram all the formulas into cheat sheet won't help you in doing these exam questions maybe except the first three points:) Anyway I believe the only possible way to do well in this exam is to study every single page of the lecture note over and over again until you could understand each proof well, then try to make a mind map for the whole subject and do as much as questions as possible.

All in all I think this is a much more theoretical subject than I expected and the contents are rather intelligently demanding comparing to other second year math subjects I've done so far, it certainly needs a lot of self learning, maybe as well as a much more understandable teaching style.
« Last Edit: September 10, 2017, 09:52:05 pm by cassiecate »
2015-2018: Bachelor of Science (Mathematics and Statistics) @ UoM
Concurrent Diploma in German @ UoM

#### Hernandes

• Posts: 9
• Respect: +2
• School: The University of Melbourne
##### Re: University of Melbourne - Subject Reviews & Ratings
« Reply #595 on: July 12, 2016, 11:47:27 pm »
+5
Subject Code/Name: ANAT30007: Human Locomotor Systems
Workload:  72 hours (3 x one hour lectures per week, 1 x three hour practical per week)

Assessment:  2 quizzes on theory and practical work throughout the semester, each worth 10% (20%);
2-hour written theory examination at the end of semester (40%);
2-hour written practical examination at the end of semester (40%)

Lectopia Enabled:  Yes, with screen capture.

Past exams available:   No. Although Varsha did put up a few sample questions.

Textbook Recommendation:

Moore KL et al: Clinically Oriented Anatomy, Lippincott Williams & Wilkins 2014
OR

Drake et al Gray's Anatomy for Students, Elsevier 2015

I didn't use either of these. And I think you get Grays Anatomy for free online, via the LMS. Dont quote me on that though.

Lecturer(s):
-Varsha Pilbrow (every thing thats not covered by the other lecturers, which is a lot)
- Peter Kitchener (Neuro stuff + Locomotion)
- Jenny Hayes (nerves/vessels of Upper limb and lower limb)
- Kylie Pickles (Spine)
- Various clinical lecturers.

Year & Semester of completion:
2016, Semester 1
Rating:  3.9 Out of 5

Ok, like one of the previous reviewers for this subject I also have a love/hate relationship for this subject. Mainly since the content you learn is so interesting, and I did love the content. But you are simply given too much content, that it makes assessment so much more difficult. And believe me I loved second year anatomy, and thought I would love this too by default, but this subject goes into crazy detail, with some very fast talking lecturers!

Now I know this subject already has quite a few reviews, but I thought a few of them made it seem like this subject was no biggie. This subject most definitely IS a biggie! Perhaps the course has changed since those reviews were posted, but I feel people need to be aware of what they're getting themselves into with this subject, with the most current info available.

Lectures:

Ok its mainly broken up into 5 bits:
-Neuroscience and Locomotion
-Spine
-Upper Limb
-Lower Limb
- Evolution
And these are interspersed with lectures by clinical lecturers, in the fields of biomechanics, radiology and surgery that I can recall.

If you've done or are doing neuroscience subjects, doing the neuroscience+locomotion lectures you'll have a slight advantage.
The spine, upper limb, and lower limb have HEAPS of info, and their lecturers I found can talk quite fast. And everything they say is examinable, so I found myself having to rewatch all these lectures in their entirety, to just make sure I didn't miss something they said. Also the lecture notes aren't very detailed, so you need to rewatch the lectures (at least I had to).

Also there's Jenny Hayes in this subject! She teaches nerves and vessels of both upper and lower limb. She's a fantastic lecturer, really helps you learn her content, she'll repeat important points, have some slides that kind've summarise the paths/innervations of nerves and vessels. Her questions on the MST were quite easy too which was good.

Evolution I thought was relatively the easiest, and the most enjoyable topic. I always liked learning about how we differ to early hominin species, as well as from the apes and monkeys. Quite Enjoyable. We also have associated practicals, where we look at skulls, and ape and monkey skeletons. Cool stuff!

The clinical lectures. A big concern for many students, was what to take from the clinical lectures, since often times they'll have heaps of lecture slides (One of them went above 100 slides!). I always tried to look for a few things, that were emphasised, and take a few key points out of the lecture, rather than write down everything said (which I felt had to be done in the non-clinical lectures). Really enjoyed these lectures too, and some of the clinicians would even invite students to come watch them in action at their hospitals. Great people!

Assessment:

MST 1. Median was roughly 21
MST 2 : Median was roughly 22

I thought the MSTs were quite fair, and are what you'd expect. You really need to put a lot of time and effort into studying them though, but unlike with the final exams, you will definitely get a payoff from all that hard work.

Practicals:

These were quite interesting, as you'd find yourself chopping into a region of a cadaver each week (except for the last few weeks), with a partner. You'd generally have about 12 people around 1 demonstrator, who'd have 2 cadavers, then you'd go off into pairs and work on a specific region on one of the cadavers. I found these quite enjoyable, as you would find things like shoulder or knee replacements in the cadavers, which were really cool to find and look at.

Exams:

Oh god, these exams. Just wow.

Okay. Well you have a practical and a theory exam, and my god with the huge amount of content you have to revise, it is so difficult to know what comes up on the exam. They seemed to focus on post MST 2 a lot with the theory exam, and the prac exam was much more evenly spread out. Boy do I wish I knew that beforehand

Prac exam was all multi choice, that were based off a series of pictures. Unsure how I went with this one. You should use anatomedia, or some atlas if you can get your hands on one for this exam. And hope that anatomedia doesn't crash the day before your prac exam, like it did with us! =[

The theory exam had 4 sections. 1st section was all Multi-choice, 2nd section was fill in the blanks, 3rd section was short answer, 4th was long answer.
I found the 1st and 4th sections quite good, but the 2nd and 3rd sections were tough. There are some questions and topics in those 2 sections that may trip you up. Felt like things that were barely emphasised, and some odd terms that weren't explained well, were assessed too greatly in these 2 sections. Sections 1 and 4 were great though.

Somehow managed to scrape an H1 in this subject, but a lot of people I knew weren't so lucky despite a lot of study.

Recommendation:

This subject certainly had a lot of interesting content, and the access they give you to cadavers is great and really helps supplement your learning. I know that's something you don't get at a lot of other universities, as unimelb has that body donor program. I feel like I have a great understanding of the human body (of course theres always so much more to learn about the body), and I really enjoyed it.
But as I said before, there is just so much information in this subject, that once you get to the final exams you'll have no idea if what areas/topics that you put the most time into, are even going to pop up. There was a lot of luck in this subject, that determined whether you'd go well in the final exams and get the coveted H1. Either need luck, or you'd better have an eidetic memory!

If you're someone thats concerned about your grades, and you're not doing a major that requires this subject, i would probably not do this subject. You can put so much time and effort into this subject, and its not going to guarantee you a high mark. This subject could distract you from other subjects, and negatively affect your performance in them.

But if you're doing the HSF Major, well you don't really have a choice. And I wish you luck

#### silverpixeli

• ATAR Notes Lecturer
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##### Re: University of Melbourne - Subject Reviews & Ratings
« Reply #596 on: July 13, 2016, 10:42:10 pm »
+3
Subject Code/Name: INFO20003 Database Systems

Two 1h lectures
One 2h workshop

Assessment:

The projects were a little different than described in the 2015 handbook, apparently we were unable to do team projects due to some technical difficulties. We had this instead:

10% Mid-Semester Test (week 7)*
2% Assessed Workshop (creating a simple database-driven website with PHP, week 7)
14% Assignment 1 (creating a large-scale data model, due week 10)
14% Assignment 2 (building a bunch of SQL queries of varying complexity, due week 12)
60% Exam in exam period

From the looks of the 2016 handbook entry, there are still no team projects, but the assessment structure has been altered somewhat.

* The in-lecture MST was aborted because Mitchell didn't bring enough test papers for everyone. It was replaced with a take-home test due about one week later.

Lecture Capture:

Yep, with screen capture. Slides were also provided on LMS. Some whiteboard work was carried out during lectures and Mitchell swore you were missing out by not being there in person, but you really weren't.

Past exams available:

No past exams or practice exams were available.

We were only given a small list of incomplete exam style questions, aimed to give us a vague idea of what kind of questions to expect.

There was also a large set of SQL query practice questions with sample solutions (kind of like a problem booklet in a maths subject, with about 45 questions total, which was okay since SQL was only a small part of the course).

We were also referred to 'everything in the lecture slides and workshops'. However, the questions on the exam mostly demanded more than what had been presented in lectures or required in workshops, and rewarded independent study.

Textbook Recommendation:

We didn't really stick to one textbook but there were a few official recommended texts: Modern Database Management (Hoffer) and Database Systems Concepts (Silberschatz). Mitchell told us that one of the changes planned for future semesters was to standardise more to the Hoffer textbook, so in future years that one might be a bigger deal.

Another notable textbook was Data Modelling Essentials (Simsion), authored by an ex-UoM Database Systems lecturer who quit his job as a high-profile academic to study screenwriting at RMIT and is the author of The Rosie Project and its sequel The Rosie Effect; books which are being made into movies soon! Anyway this textbook was relevant to a small section of the course on evaluating the quality of data models.

Unfortunately there seems to be a trend of verbosity in Database Systems textbooks, which can contain a lot of text (700-1000+ pages) but not say very much at all. I personally found there was no need to waste so much time to communicate such an inherently logical topic. So I've listed some alternative recommendations here that might end up being preferable!

First up, I found a much more concise resource in Database Fundamentals, a free book from IBM. It talks about a different Database Management System than the one studied (DB2 instead of MySQL) but the first few sections cover the essentials of relational databases really nicely, and without wasting your time with pages of useless fluff. Worth reading carefully!

I found the Wikipedia articles on database systems concepts comprehensive and insightful, probably because it's such a well-established field. Generally, though, searching the web proved a good way to answer any questions that came up during study.

I'll also take this opportunity to plug my own set of notes, available on StudentVIP for $19 (I get$14.25 ). I put these together in the lead up to the exam; they're are the result of my efforts to piece together the subject into a coherent whole. I'm also open to responding to PMs if anyone is really struggling.

Lecturers:

The majority of lectures were delivered by the course coordinator Mitchell Harrop (the first 7 weeks, and also week 12 for revision). Mitchell's topics included introductory concepts, 3 weeks on data modelling, and also SQL, data model quality, and using databases with websites. Mitchell is a relatively new lecturer and his inexperience really showed.

Then there were 4 lectures on miscellaneous topics by Greg Wadley, including normalisation, transactions, and select database administration topics. These lectures were put together really nicely.

There was a guest lecture on Data Warehousing (a.k.a. big databases) from Sean Maynard, the previous lecturer for this subject. I was glad he was not still the lecturer for the subject.

And finally, 3 lectures on relational algebra and query processing by Linda Stern, however this was Linda's last semester lecturing before retirement and I believe these topics are being removed from the course for future semesters. Linda's lectures were organised nicely but her explanations sometimes missed points and weren't always so satisfying.

Year & Semester of completion: 2015 Semester 2

Rating: 1/5

Database Systems is on the road for the Computing and Software Systems major, as it is a prerequisite for the capstone subject COMP30022 IT Project. It might also be considered by anyone interested in information systems. However, based on my experience in 2015, I would recommend avoiding this subject for a few years if possible, until the coordinators figure out how to run a subject coherently.

For the most part, there was less than zero structure in this subject's delivery. Expectations were poorly communicated, and constantly changing. Lectures and workshops were a waste of time. This meant that it was almost impossible to study independently, because nobody knew what we were meant to be studying. Projects were deliberately ambiguous (to 'simulate the real world', despite how in the real world you get to ask for clarification from clients; they don't say 'I can't tell you how to do the project' and force you to make assumptions about what they want), and the MST (the one that was cancelled) and exam were incredibly unbalanced (requiring highly specific knowledge and also containing large modelling tasks better assessed through a longer-term assignment).

Above all, INFO20003 was utterly intellectually dissatisfying; a real shame considering the potential of databases as a genuinely interesting topic of study. The theory of relational databases is inherently mathematically precise and completely logical. The systems that implement that theory are filled with clever algorithms and data structures worth studying. Data modelling in itself may not be a black-and-white problem-solving discipline, but there are logical approaches and sound principles that can be used to construct and evaluate quality data models. Instead, INFO20003 abandoned this rich content in favour of a shallow 'learn by example' approach that skipped almost all of the interesting theory underying the topic of database systems.

I'm not sure if it would have been possible, but I wish I had looked into taking INFO90002 Database Systems & Information Modelling in its place. The coordinator for this graduate-level version is Greg Wadley, who also gave 4 of the INFO20003 lectures. His lectures stood out as a shining example of how good this subject could have been.

If you're stuck taking this subject, and it hasn't had its deep fundamental issues addressed by the time you take it, my advice is to attempt to take your learning into your own hands as much as possible.
- For data modelling, focus on nailing the underlying principles of the relational model. These principles were never mentioned (whether the lecturer was aware they existed or not I don't know) but they were the basis for everything that we learned and did, and once you understand them, data modelling is only as hard as learning the notation and applying your common sense to interpret case studies.
- For learning SQL, there's no shortage of online tutorials, from W3Schools to Codecademy's short course and I'm sure there are plenty of MOOCs and other resources I never tried.
- For the rest of the subject (the conceptual stuff), use your usual strategies (making notes, flashcards, or practice problems, whatever) and try to dig a little deeper than what's mentioned in lectures because the exams sure tested beyond the level discussed.
In short, find a good learning resource (see textbook section), because if you're not relying on lectures, it should be entirely possible to survive this subject's endless frustrations.
« Last Edit: July 14, 2016, 12:49:32 pm by silverpixeli »
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#### Gentoo

• Victorian
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##### Re: University of Melbourne - Subject Reviews & Ratings
« Reply #597 on: July 14, 2016, 12:44:01 pm »
+6
Subject Code/Name: MGMT20001 Organisational Behaviour

Workload: One 1-hour lecture (most are in-person, but 3 are online-only lectures), one online tutorial (takes approx. 1 hour), one 1-hour in-person tute

Assessment: 10% individual assignment, 30% group assignment, 10% tutorial participation, 50% 2-hour Exam

Lectopia Enabled: Yes, with screen capture

Past exams available: No, but one sample exam (which involved theory that was not taught in the course and a case study which was also not taught in the course i.e. not particularly helpful)

Textbook Recommendation: University compiled textbook “Organisational Behaviour” – relatively useful and had some interesting case studies in it, although it occasionally taught the material in a different way to the lectures which can be a cause of confusion

Lecturers: Angela McCabe (in-person & online), Dr Joeri Mol (in-person & online), Graham Sewell (online)

Year & Semester of completion: 2016, Semester 1

Rating: 2.75/5

OB is, both in a literal and euphemistic sense, an interesting subject. On the one hand, the content itself is intrinsically interesting and regularly feels applicable to real life. As such, it’s easy to see why this is a compulsory commerce subject; it teaches you perspectives that are relevant wherever you end up. In addition, the subject is expertly coordinated (the LMS is well resourced and the ‘OB team’ regularly e-mails students with updates and reminders on what has to be done at any given point in time) and generally well taught.  Unfortunately, the assessment is deeply flawed and sours the overall experience of the subject somewhat.

The content:
OB goes into the factors that affect how groups and organisations perform. The course is split into two parts: the ‘micro’ part which zeroes in on individual and group processes and has more of a psychological perspective (perception, teams & leadership, group processes etc.), and the ‘macro’ part which takes a broader view of organisations as a whole and has a more sociological/business perspective (change, communication, culture, strategy/structure).

In the macro part of the course, each topic was taught alongside (in the tutorials and occasionally the lectures too) a particular case study that demonstrated how the concepts taught can affect organisational performance. The case studies are entirely true to real life and always interesting; you learn about the spectacular downfall of Enron, the tumultuous history of Apple, etc. The theory + case study combination works very well for the subject.

The theory is usually interesting, although it can get mired in using jargon to describe really obvious, simple stuff (e.g. most of the steps in a ‘change plan’ are just common sense) which can become a little tedious given that you have to memorise it.

Lectures:
While I can’t speak for Joeri (the other in-person lecturer), Angela was an engaging lecturer who communicated the material clearly. She made a genuine effort to connect with the audience, which was nice, especially in the dryer bits of the course.

As for the 3 online-only lectures, these were a mixed bag. The first one was taken by Graham Sewell, which was pretty much down-the-line in terms of how the content was taught and had an interesting interview with a high-ranking police officer to go along with the theory. The latter two ones were in part presented by Angela and Joeri and in part by a hand-drawn animated comic-type presentation narrated by someone else. These were… weird. Angela and Joeri seemed oddly stilted in front of the camera during their sections, and the animated sections involved these whimsical stories that tried to demonstrate the week’s theory; think an alien from outer space coming to earth and observing penguins in their natural habitat to learn about ‘culture’, or some ancient Greek mythology character who did… something in an effort to learn about ‘communication’ (I don’t quite remember, but it was weird). The animation was actually pretty neat, but the lectures as a whole were disorienting and at the end of watching them, it felt like I had stumbled out of some bizarre, strangely educational dream of which I had little memory. Fortunately they also release a plain-English script of the lectures, which come revision time for exams is probably more helpful.

Online tutes:
The online tutes ask you questions about the week’s lecture material and are effective at testing you on your knowledge. You’re meant to complete them some time before your in-person tutorial each week and I think you need to satisfactorily complete them to get tute participation marks (your tutor can see whether you’ve completed it or not); regardless, they’re worth doing anyway.

In-person tutes:
In your tutes, you work in groups to apply the week’s theory to the corresponding case study and your answers are presented to the class. The tutor also recaps the week’s lecture. How much you get out of these is a function of whether you learnt the material properly before coming (including reading the case study) as well how switched-on the other people in your tutorial are and how engaging your tutor is.

Assessment:
The assessment can be frustrating and stressful. It often feels like a good chunk of your grade is out of your control and that there isn’t a strong enough relationship between how much work you put in and your result at the end of the day (something which is particularly concerning for a subject that they’re forcing everyone to do).

Individual assignment:
It’s just a typical 1000 word research assignment, but completing it was about as interesting as watching paint dry. They managed to pick out the one topic from the entire course that was really boring (contrasting ‘management styles’, which was oversimplified to the point where it basically lost all meaning) and as such, doing it was a real chore. What was a positive is that the OB faculty ran workshops to help students with details such as APA referencing, formatting etc. which again speaks to the fact that the subject is well organised.

Group assignment:
At least watching paint dry isn’t stressful. I’ll leave the unpleasant simile of choice up to the reader (I can think of a few), but this was an ordeal. The sheer enormity of the task (5000 words is a lot, especially when the theory that you’ll be analyzing the case study with is only 1 lecture’s worth), combined with the fact so much of your mark (30% of your overall grade!) is determined by other people’s work is just… eugh.

Ostensibly, results on a pseudo-psychometric test questionnaire thing are the basis of how your tutor splits up the people in your tutorial into groups of 4/5, but I’m pretty sure our tutor just allocated people into groups arbitrarily. Either way, it’s going to be tough to self-select yourself into a group of friends, even if they’re in your tutorial. For all intents and purposes, you have no control over the people who will have a large impact on your mark.

What made this assignment bearable was the fact that the content itself was genuinely interesting: the assignment required you to apply psychological theories and perspectives to analyse what could be going wrong with the candidate selection/retention/HR process at a particular firm, and asked you to provide recommendations on how to fix these issues. The process of applying the theory to the case study was interesting, which again demonstrates the contradictory of the subject: good content, bad assessment.

Exam:
The exam consisted of 4 essay questions: 1 on a micro topic which asked you to relate one of the micro theories to your experience working in the team assignment, and 3 questions on how a given macro topic related to one of the case studies.
Prior to the exam, we were given a list of the possible combinations of macro topics and case studies that could be tested (there ended up being about 12), which made the process of studying for the exam a little easier, but the fact still remains that you’re forced to learn 4 macro topics and 4 case studies, yet you’ll only be tested on one of each. Essentially, every time you sit down and study, there’s a 75% chance that what you’re learning will be useless in determining how well you do; not exactly the most motivating thought come SWOTVAC. It also increases the luck/variance involved; if you happen to get tested with a topic that you like, you’ll likely do better than if you’re tested on a topic which you didn’t really engage with as well.

Overall:
If you’re a commerce student, this review isn’t particularly instructive; you’ve gotta do it anyway. What I would stress is to approach it with a positive attitude whenever possible; the mere mention of OB is usually enough to elicit groans from students and I think this negativity is contagious and self-perpetuating to some extent. I do genuinely think the subject has interesting, useful material, so try and enjoy it for what it is while not letting the issues with the assessment consume you.

If you’re looking to do OB as breadth I wouldn’t really recommend strongly for or against doing it. If you’ve got a relatively high tolerance for slightly open-ended essay-based assessment, group work and exams that test you on a fraction of the course, then you might enjoy and get something out of it. If you don’t, then you probably won’t have a great time, but it could still be useful.

#### Maths Forever

• Victorian
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##### Re: University of Melbourne - Subject Reviews & Ratings
« Reply #598 on: July 20, 2016, 01:55:19 pm »
+2
Subject Code/Name: CHEM20018: Chemistry: Reactions and Synthesis

Workload: 3 x One Hour Lectures (all at 8am this semester) and 1 Tutorial per Week

Assessment: Online Continuous Assessment Tests (20%) and 3 Hour Written Examination (80%)

Lectopia Enabled: Yes, with screen capture.

Past exams available: Yes, but no solutions are provided. You can access 2009 to 2015 semester one papers on the Baillieu Library site.

Textbook Recommendation:   There are no prescribed texts, but there are 3 recommended texts listed in the handbook which cover Organic Chemistry, Physical Chemistry and Inorganic Chemistry. If you are planning to go on to third year chemistry or CHEM20020: Chemistry: Structure and Properties in semester two, it is worthwhile buying these.

Lecturer(s): Week 1 to 3 (Prof. J. White), Week 4 to 5 (Prof. K. Ghiggino), Week 6 to 7 (A/Prof B. Abrahams), Week 8 to 9 (A/Prof. P. Donnelly).

In Week 10 to 12 there is an option. Option I: Prof P. Mulvaney. Option II: A/Prof S. Williams.

Year & Semester of completion: 2016, Semester 1

Rating: 5/5

Comments: Overall, I found this subject to be very enjoyable and there were some topics which were extremely engaging and made me feel extremely motivated to learn the content. I will discuss the course in sections (since it has changed somewhat since the last two reviews).

It should be noted in particular that the online tests now only allow for 1 ATTEMPT. Although, you get more than enough time to complete each online test (1 hour and 30 minutes) and they each consist of only about ten to twelve questions (most of which are multiple choice). These tests contribute for 20% of your overall mark but only your best 4 out of 5 tests will be averaged and counted (so you can afford for one bad day . Be aware however that some of the questions require a numerical answer, which means you need to get within a tolerance range that is set by the lecturer. If you take these tests seriously, you will set yourself up well going into the exam if you earn almost the full 20% for the tests. There is one test for every topic in CHEM20018 (so five in total).

Since there are no answers to practice exams, I recommend setting up a Facebook page for this subject. In 2016 there were so many contributors and by the time of the exam, we pretty much had solutions to the 2013, 2014 and 2015 exams. It is a great way to learn from each other and I highly recommend it!

Week 1 to 3: Organic Compounds (Lecturer: Prof. J. White)

Professor White was a fantastic lecturer. The whole three weeks are devoted to enol/keto tautomerism chemistry. If you get plenty of chances to practice on this section it will be an easy part of the exam (and one you can knock out quickly!). All seven past exams have almost exactly the same format. All that varies is the compounds. So you pretty much know exactly what to expect when walking into the exam. Although you don’t need to draw these in the exam, it is very helpful to understand the mechanisms for each of the sub-topics. These sub-topics include drawing enol forms, electrophilicity and nucleophilicity of enol/keto forms of carbonyl compounds, stereochemical considerations, halogenation, alkylation, Aldol reactions (including the formation of cyclic compounds and mixed Aldol reactions), Claisen reactions, Malonate and Acetoacetate chemistry and Michael Chemistry. Once again, I cannot emphasise the value of practice exams (despite there being no answers).

Week 4 to 5: Thermodynamics (Lecturer: Prof. K. Ghiggino)

Seeing as I also study Physics, I didn’t find this section to be too difficult. About half of the time you are just reviewing some of the content covered from first year chemistry in CHEM10003. As Ken mentioned, a lot of this content is setting you up for the topics which are to follow in this subject and later years. The sub-topics include Heat, Work, Internal Energy, Enthalpy, Irreversible and Reversible Processes, Isothermal Processes, Adiabatic Processes, Free Expansions, Heat Capacity (including Constant Pressure and Volume Heat Capacity), Hess’ Law, Entropy (including Phase Changes and that of Isothermal Processes), Gibbs free energy, the Carnot cycle. We missed out on one of the lectures due to Good Friday. However, Ken still covered all of the material in 4 lectures, which left the final lecture to go over the Thermodynamics section from the 2015 exam. Again, if you know what you are doing this should not be a very difficult part of the exam.

Week 6 to 7: Thermodynamics of Inorganic Reactions (Lecturer: A/Prof B. Abrahams)

This was A/Prof Abrahams’ first time taking this part of the course. It was previously taken by Dr S. Best. Dr Best takes seven lectures of the CHEM20020 course. So I will know what he’s like after next semester. However, I feel that A/Prof Abrahams did an amazing job at teaching this section and personally this was my favourite part of the course. Brendan made a point of simplifying things compared to previous years (in which it used to be quite difficult and generally not a popular part of the subject). The sub-topics included a review of crystal lattice types from first year (which Brendan also teaches), the Born-Harber cycle, Lattice Enthalpies (calculated experimentally using the Born-Harber cycle and estimated using the Born-Lande, Borm-Mayer and Kapustinskii equations), Thermal Decomposition Stability, Trends in Oxidation State, Solubility of Ionic Compounds, Metal Oxides, Ellingham Diagrams, Latimer Diagrams, Disproportionation and Comproportionation Reactions, Frost Diagrams, Field of Stability of Water, Pourbaix Diagrams and Effect of Complexation on E° values. There is a lot of content, but Brendan has a way of making it seem interesting. I also felt that I could follow what Brendan was saying quite easily, which isn’t always the case with some lecturers.

Week 8 to 9: Coordination Chemistry (Lecturer: A/Prof. P. Donnelly)

Paul’s part of the course gives you a real taste for how coordination compounds can be used in both inorganic synthesis and biological applications. It puts into practise all that you have learned at the end of first year. We start by learning about the stability of metal ions (Irving-Williams series) and of ligands (chelate effect and macrocyclic effects). You also apply this briefly to thermodynamics. Other topics include Hard-Soft Acid-Base Theory (and its applications to Mercury Poisoning), Kinetic Lability of Coordination Complexes (including pathway types in synthesis), the world’s leading anti-cancer treating agent ‘cisplatin’, the ‘template’ effect (i.e. using a metal ion to carry out the organic synthesis of large cyclic molecules) and the use of Carbonic anhydrase to effectively maintain carbon dioxide levels in the bloodstream. There is a lot to remember in these two weeks of the course, but the content is interesting and Paul makes the content of his lectures highly engaging (including the use of some practical demonstrations).

Like I mentioned above, in Weeks 10 to 12 there is a choice. I choice Option I: Chemistry of Materials (Prof P. Mulvaney) so I will discuss this option. The other choice is Option II: Biological Organic Chemistry (A/Prof S. Williams).

This was the first time ‘Chemistry of Materials’ was taught in this course. It used to be called ‘Theory of Advanced Materials’ (taught by Dr. A. Gray-Weale). However, Angus took a more thermodynamic approach to these three weeks (since he was a statistical chemist). Paul took a very different approach and because of this there were no past exam sections that were of any use for his section. However, he did provide plenty of tutorial questions and additional questions during SWOTVAC. He also made it very clear what he expected everyone to understand after his three weeks of the course. The first week was spent on chemical bonding. We reviewed unit conversion, Ionic Bonding (somewhat similar to what Brendan went over in week 6), Covalent Bonding, Metallic Bonding and Van Der Waal’s Interactions (i.e. secondary bonding). In the second and third week we learnt about band structure and its applications. This included band gaps of intrinsic materials, doping to change the properties of materials, the similar relationships between band gaps and redox potentials, conductivity calculations and how solar cells work and are developed. Just also be aware that the tutorial questions and solutions are NOT posted on the LMS. To receive the questions you must attend the tutorial.

Overall, I think option I is designed for more ‘physics/engineering’ orientated students whilst option II is designed for more ‘biological’ orientated students. Both sets of lectures are recorded (which is useful since they run at the same time – 8am).

I found my experience in CHEM20018 to be very enjoyable and I would highly recommend the subject if you enjoyed first-year chemistry.
« Last Edit: July 27, 2016, 08:17:38 pm by Maths Forever »
Currently studying at the University of Melbourne.

#### Nightwing

• Trailblazer
• Posts: 27
• School: Melbourne High School Grad Year: 2013
• Respect: +2
##### Re: University of Melbourne - Subject Reviews & Ratings
« Reply #599 on: July 22, 2016, 02:17:34 am »
+7
Subject Code/Name: ENGR20004 Engineering Mechanics

Workload:  3 x 1 hour Lectures (Weekly)
1 x 2 hour Workshop (Weekly)
(If done over Summer Sem, double the workload a week)

Assessment:  4 x 7.5% Assignments (2 each on Statics & Dynamics)
2 x 7.5% Tests in Week 6 & 12
5% Weekly Online Quizzes
50% Exam (Hurdle)

Lectopia Enabled:  Yes, with screen capture.

Past exams available:  4 Past exams available on library database, however, since the lecturers are vastly different each semester, only 2 or so would generally be entirely relevant to you. The others can be used as extra practice if you want. (No solutions though).

Textbook Recommendation:  None officially. But Statics & Mechanics of Materials – Hibbeler (3rd Edition) as well as Engineering Mechanics: Dynamics – Meriam & Kraige (6th Edition) can be used as extra practice. Personally found the Statics textbook fairly helpful, but not the Dynamics as much.

Lecturer(s): Dr. Cheng Chin (Both Statics & Dynamics)

Year & Semester of completion: Summer Semester 2016

Rating:  5 Out of 5

Simply put. This subject is tough. For most of you doing this as a core (perhaps not the Mechanical Majors), this will likely be the hardest subject in your undergrad, and is also coincidentally a pre-req for most of your third year subjects. Generally speaking, around 1/3 people will end up having to redo this subject. Many of the people in my class were those who had failed in Sem 2 2015, so as Hancock said in his review which I'd recommend checking out (Hancock's Review), it’s not a class to screw around in, and definitely not one you should consider winging. Please don’t. One consolation that you can take from Hancock's review is that the subject has been made a fair bit easier since his time, with parts of certain topics like Vibrations in Dynamics, and Eccentric Loading in Statics becoming much more simplified.
One thing I will add though is that unlike some other subjects, Eng Mech is possibly the fairest subjects I’ve done so far. What I mean by that is the mark you get at the end will be a direct representation of the effort and time you put in throughout the entire semester, unlike some others where you can get away with being a tad lazy, or where you can put in tonnes of work and still end up with a meh score (ahem, looking at you Calc 2). Due to the frantic pace at which it is taught, falling behind in this class is not something that I’d advise in the slightest, or SWOTVAC will be a nightmare.

The subject is split into two topics; Statics, and Dynamics. Both of which are 6 weeks long. Statics effectively starts off where the Mechanics module in ESD2 left off, with methods of joints and the like with trusses.
The new content in Statics begins with:
-   Method of sections: a different way of analyzing reaction forces in trusses
-   Shear Force Diagrams (SFD) and Bending Moment Diagrams (BMD) for differently loaded beams
-   Stress, Strain, Poissons, Hooke’s Law
-   Shear Stress, Shear Strain, ….
-   Thermal Stress
-   Torsion
-   Power
-   Flexure
-   Bending
-   & finally, Deflection
As you can see, tonnes to learn, and this is only in the first 6 weeks. The content itself though is not too difficult to learn so long as you keep up to date with the lectures, though it does get slightly harder from around Torsion onwards. But I cannot stress this enough. Keep doing questions from whatever resource you can find. Every week, you are given a set of around 15-20 “Tutorial” questions (don’t even ask, made no sense to me either) that you can attempt at home for Statics on top of the Workshop questions. Though a few of the questions are beyond the course expectations, at least attempt every single one, and ones you can’t do or don’t understand, ask one of your tutors the next week. I reckon I bugged my tutor with at least 3-4 questions I was struggling with each workshop. You prepare yourself by doing these questions, you should not have any major issues with anything you do in the subject.

The Dynamics portion builds upon VCE Year 12 Physics for those of you who did it, and Physics 1. Much of the content is stuff that you would have covered in some capacity before, but likely not to a significant difficulty. Eng Mech covers all of it in much more detail, and something I really liked compared to the Statics section is that often you had to first think and figure out how the system you were given was going to work, before you started any calculations, whereas in Statics at times things got a tad monotonous with just plugging values into equations. The topics covered were:
-   Kinematics
-   Relative Motion (Polar, Rectangular, N-T Co-ordinate Systems)
-   Particle Kinetics
-   Work
-   Impulse & Momentum Angular Impulse & Momentum
-   Impact
-   Forced Vibrations
-   Rigid Body Motion, Absolute Motion analysis, Relative Velocity
-   Instantaneous Centres, Relative Acceleration
-   General Planar Motion
Whilst there aren’t as many topics covered in Dynamics, the content is much harder to understand, and I think for most people, it’s the inability to visualise what is going on. Often you’re left sitting there looking at the question thinking where do I even start. Rigid Body Motion onwards in particular can be horribly annoying to try and do at times. Again unfortunately I have to say the only way to get better at it is to do tonnes and tonnes of questions.

Whilst some other people I know who did Eng Mech had issues with the lectures, I really liked them. Lectures were very well organised and thought out. Though perhaps sometimes there was too much content crammed into a single hour. One massive thing I loved about the Eng Mech lectures is the way they are split up. In a normal semester, the first two lectures in the week would be intro lectures where you are taught the concepts, and the theory, whilst the third lecture is doing examples on the concepts taught in the two previous lectures. Though for me doing it over Summer meant it was two weeks crammed into one, and often 3 lectures in a single day, the method of introduction, followed by application and consolidation in that third lecture is something I’m a big fan of, and wish more Engineering subjects adopted this approach, rather than just droan on and on with theory. Also side note, don’t bother with the “Tutorial” 13 questions. Just a waste of time. Stuff is ridiculously hard, and there is no way any of it is going to come up in any assessment.

The workshops too were something that I absolutely loved, with again in particular the way they are set up being one of the best parts. Workshops are officially run across 2 hours, but the minimum recommended time you stay for is the first hour unless there is some experiment work to do for one of the assignments planned for the second half. Workshops usually start off with the head tutor doing a quick run down of the content covered in the lectures for the week again, followed by two generally fairly difficult questions which are explained fairly thoroughly and worked through by the two tutors. All this gets done in the first hour beyond which you are free to leave, or stay behind and get some help from the tutors regarding the questions done in class, or other questions you may have. I strongly, strongly recommend staying back and asking the tutors questions. It is extremely rare that tutors are this approachable, or more so this free to help with any queries, so make full use of them. I reckon I kinda got lucky and had two fairly awesome tutors who knew the content really really well, but especially the revered Engineering God Hancock who more than anything for a subject as tough as Eng Mech was able to basically dumb down and explain the concepts from the point of view of a student. Often explaining the concepts better than the lecturer himself. Though a few other people’s experiences varied, the vast majority of people I talked to always reckon the Eng Mech tutors as a whole are generally some of the best you will have in your course. I can honestly say that a massive reason to me doing half decent in this subject was due to the help Hancock gave me every single Workshop. (Hancock, if you’re reading this, sorry if I bugged you too much).

TL;DR. I absolutely loved the subject. It is by far the hardest I’ve done so far, but the quality of the lecturer, and tutors, as well as the organization of the subject as a whole meant that it was a subject that you actually enjoyed doing the work (at least in hindsight). Just ensure you keep up to date not only rocking up to lectures, but especially doing questions consistently, and ensure you’re on top of everything during semester when you still have the time so that come SWOTVAC, all you need is a quick refresher and you’re good to go for the exam.

Also, on a final note. What I’ve heard from those repeating the subject over Summer is that Semester 2 is kinda suicidal with Daniel Chung being head of the subject. Having done some of his past exams for revision, it’s hard not to agree. His exams don’t even nearly compare to Semester 1 or 2. If you’re willing to give up 2 months of your summer break, do it over summer. Else I’d say do it in Sem 1 if you can. The standard in Summer or Semester 1 is more than enough for your third year subjects where you expand on many of the concepts learnt in Eng Mech, and it's not worth the likely WAM drop.
« Last Edit: July 23, 2016, 01:39:56 am by Nightwing »
2012: BM
2013: Physics, Spesh, Methods, English, Economics
ATAR: 97.55

2014 (Sem1): BEnvs
2014 (Sem 2) - 2017 (Sem 1): BSc (Mechanical & Civil Systems)
2014: ENVS10003, ENVS10007, ENVS10001, ENGR10004, MAST10007, PHYC10004
2015: COMP20005, MAST10006, BLAW10001, ENEN20002, ENGR20003, ENGR10003
2016: ENGR20004, MCEN30017, MAST20029, CVEN30008, EVSC30003, MCEN30014, CVEN30009, MCEN30020, SCIE20001

If you need any help or advice, PM me and I'll be happy to lend a hand