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December 10, 2018, 09:33:06 pm

Author Topic: UNSW Course Reviews  (Read 20701 times)  Share 

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RuiAce

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Re: UNSW Course Reviews
« Reply #75 on: November 27, 2018, 03:38:34 pm »
+6
Subject Code/Name: MATH3871 - Bayesian Inference and Computation

Contact Hours: 2 hours of lecture, 1 hour of tutorial, 1 hour of laboratory

Assumed Knowledge: MATH2801 or MATH2901, but the latter is seriously recommended. (Apparently the lecturer was told by someone that MATH2931 was also a prerequisite when it was not, but fortunately he kept the 2931 content minimal. Although even if not mandatory, MATH2931 is still helpful.)

Assessment:
- 20% Group Assignment
- 15% Individual Assignment
- 5% Class Participation (not too hard to get)
- 60% Final Exam

Lecture Recordings? Mostly yes - at times Zdravko used the whiteboard, but not frequently.

Notes/Materials Available: Lecture slides (+ notes for the MCMC section) and tutorial/lab exercises provided, but that was it. Felt insufficient, but it seemed to be fine - you just had to be able to redo the tutorial exercises.

Textbook: Statistical Modeling and Computation, D.P. Kroese and J.C.C. Chan, Springer, 2014. Was not necessary but it was still a decent textbook.
Also provided was Handbook of Monte Carlo Methods, D.P. Kroese, T. Taimre, Z. Botev - had some helpful techniques included.

Lecturer(s): Dr. Zdravko Botev

Year & Semester/Trimester of completion: 18 s2

Difficulty: 3.5/5

Overall Rating: 4.5/5 

Your Mark/Grade: 92 HD

Comments:
This is one of the third year electives for a Statistics major. Completion of this course along with the three core gets accreditation with the Statistics Society of Australia.

Bayesian inference stems from a probabilistic approach of inference - it literally falls out of Bayes rule. In the classical frequentist approach, parameters to be estimated were fixed, but Bayesian approaches treat the parameter itself as a random variable, consequently invoking lots more probabilistic techniques (credible intervals, hypothesis tests, expectation of the parameter, predictive distribution etc.)

This course also introduced simulation techniques. Basic methods (inverse transform, accept/reject method) were covered but there was a lot of depth put into Markov-chain Monte Carlo.

The computations in this course are quite interesting. On one hand, some of them are fairly straightforward thanks to the shortcuts you're introduced in weeks 1 and 2. But then at other times they get completely chaotic and it feels a bit like a war trying to fight through all of it (cough Bayes factors). A part of the course was recognising distributions, because that helped you simplify down nasty integrals (including multivariate integrals).
Those tricks were so convenient though. Trivialised pretty much half of the computations you saw in this course.

The simulations were examined through making you do a few computations in advance and also writing pseudocode. For example, with the usual rejection sampling you had to understand high school optimisation to find the optimal enveloping constant. But you pretty much just had to adapt your distributions/values/etc. to the algorithm itself to write out the pseudocode, and there was no strict style guide for it either.

Much like with combinatorics last sem, I found I actually liked this course despite having various difficult concepts. It helped that the tutorials/assignments/exam were all made fairer by the new lecturer (this course used to be a 5/5 difficulty course). But it was still pretty easy to get lost in the lectures because the lecture examples were much harder to grasp (a lot of multivariate computations).

You did need to know all the definitions, techniques and tricks the course teaches you to do well in the exam. A bit of all of that was asked.

MLov

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Re: UNSW Course Reviews
« Reply #76 on: November 27, 2018, 08:15:41 pm »
+6
Subject Code/Name: MATH3821 - Statistical Modelling and Computing

Contact Hours: 2 Hours of Lecture, 2 Hours of Laboratory

Assumed Knowledge: MATH2831/MATH2931 is prerequisite

Assessment:  2 * 10% Assignments, 20% Mid-semester (Lab) Test and 60% Final Exam

Lecture Recordings?  Yes

Notes/Materials Available: N/A

Textbook: N/A

Lecturer(s): Dr. Pierre Lafaye de Micheaux

Year & Semester/Trimester of completion: 2018/2

Difficulty: Depends entirely on your effort, but I would say it is pretty easy 2/5

Overall Rating: 4/5

Your Mark/Grade: HD

Comments:

If you think this is an easy computing course, then you've walked yourself to the wrong door. MATH3821 is a *Math* course, so it is bound to have massive amount of theory (mathematical proofs). It serves as an introduction to statistical modelling (mostly regression analysis). It is *very* fast paced and it gives a brief overview of parametric and non-parametric modelling method (both theory and computing), as well as introducing Bayesian inference and Monte Carlo simulation. The main software used in this course is R (or R-studio, but be sure to know how to use R-Markdown for assignments. Iirc, Python is also acceptable for assignment, but you may be asked to write R codes for the final exam).

This course is fairly easy as long as you put efforts in, but it is very daunting if you leave it until the last minute (good luck catching up 800+ slides). Unlike level 2 statistical courses, this courses has huge amount of content (it pretty much covers the entire 2931 in the first week), which is really easy for students to lose their motivation. There is a large variability in terms of marks distribution: several high (even full) marks but the average is really low (probably a lot of slackers). Which is probably why there are mostly bad reviews across the internet.

To summaries, this course gives you a glance at what is statistics (unless you are satisfied with just linear models) and it is fairly easy to achieve high grades if you put slightly more effort than you use to.

MLov

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Re: UNSW Course Reviews
« Reply #77 on: November 27, 2018, 08:37:44 pm »
+7
Subject Code/Name: MATH3901 Higher Probability and Stochastic Processes

Contact Hours: 3 Hours Lecture and 1 Hour tutorial

Assumed Knowledge: Theres a lot haha

Prerequisite: MATH2901 or MATH2801(DN) and MATH2501 or MATH2601 and MATH2011 or MATH2111 or MATH2510 or MATH2610.

Assessment:  3 * 5% class tests, 25% mid-semester exam and a final exam. (yep, no assignments)

Lecture Recordings?  Yes

Notes/Materials Available:  N/A

Textbook: Introduction to Probability Models by Ross

Lecturer(s): Dr Gery Geenens

Year & Semester/Trimester of completion: 2018/1 (i know, my memory sucks)

Difficulty: 2/5

Overall Rating:  5/5

Your Mark/Grade: HD

Comments:

Level 3 statistic courses usually have a lot of contents (700+ slides iirc), you will learn a lot of interesting stuff like Markov chains, Queueing theory, Branching process etc. It is fun and challenging, you would learn a lot of new ways to solve probability related questions. If you are doing ACTL/Adv Sci (Math), it is pretty much a revision of ACTL2102 without time series. But be sure to be familiar with brownian motions, stochastic differential equations (SDEs) and martingales, coz ACTL3182 pretty much assumes them and straight away jump into the derivation of Black Scholes models and other stochastic models.
« Last Edit: November 27, 2018, 08:44:23 pm by MLov »

MLov

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Re: UNSW Course Reviews
« Reply #78 on: November 28, 2018, 09:06:03 pm »
+5
Subject Code/Name: ACTL 3182 - Asset-Liability and Derivative Models

Contact Hours: 3 Hours Lecture, 1 Hour Tutorial

Assumed Knowledge: ACTL2111 and ACTL2102

Assessment:  20% Assignment, 20% Mid-semester Exam, 60% Final

Lecture Recordings?  Yes

Notes/Materials Availablehttps://www.business.unsw.edu.au/degrees-courses/course-outlines/ACTL3182#course-resources

Textbook: https://www.business.unsw.edu.au/degrees-courses/course-outlines/ACTL3182#course-resources

Lecturer(s): Dr. Jonathan Ziveyi

Year & Semester/Trimester of completion: 2018/2

Difficulty: 4/5

Overall Rating:  4/5

Your Mark/Grade: BAD (idk... marks not released yet, but not confident) ;( (... okay I got a HD surprisingly, but I do reckon I did pretty bad in the finals)


Comments:

This course is mostly about valuation of assets and financial derivatives. You will go through the whole derivation process of CAPM and APT model (using Modern Portfolio Theory and factor models). Then you will be introduced to the Fundamental Theorem of Asset Pricing and go through the derivation process of the infamous Black Scholes model that you've always heard about, and close the course with interest rate models. The entire second half of the course is on stochastic process and solving SDE's, so be sure you are familiar with martingales and brownian motions (If your lecturer decided to skip those during ACTL2102, ...., good luck.)

The course is very enjoyable to do (except for the exams, Ziveyi expected HD average out of all of us, cough cough, but we 'slowly cooked' ourselves to a 50% average.), you get to build a portfolio of your own choosing for the assignment and see your CAPM fails miserably in predicting expected returns! But do expect lots of math in this course (Its Ziveyi, what else would you expect?).
« Last Edit: December 07, 2018, 08:10:05 pm by MLov »

MisterNeo

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Re: UNSW Course Reviews
« Reply #79 on: December 03, 2018, 06:51:59 pm »
+5
Subject Code/Name: ACCT1511 - Accounting and Financial Management 1B

Contact Hours:  2 hour lecture every week, 2 hour tutorial every fortnight.

Assumed Knowledge: ACCT1501

Assessment:  20% Team Quiz (in tutorials), 20% Individual Quiz (after team quiz in tutorials), 60% Final

Lecture Recordings?  No, they have recordings from past years.

Notes/Materials Available:  Same textbook as ACCT1501, weekly student handouts/readings.

Textbook: Same as ACCT1501

Lecturer(s): Dr Per Tronnes, Dr Hien Hoang, Victoria Clout, Brian Burfitt (in that order)

Year & Semester/Trimester of completion:  18s2

Difficulty: 2.5/5

Overall Rating:  3/5

Your Mark/Grade: HD

Comments:
Overall, this course was alright compared to ACCT1501.
Doing 1A was maybe a bit more challenging since we were introduced to new concepts such as double-entry accounting, so in my opinion, 1B was a bit easier.
They explored further into the recognition of assets, liabilities, equity, revenues and expenses.
20% of your marks come from a Team Quiz, and luckily I got assigned to a good group who do their readings and homework.
Both team and Individuals Quiz are based directly from the homework questions, so you MUST do them. (The fortnightly tutorials makes it easy to forget about the homework but you should practise often.)
The format of the final exam was the same as 1A with 60% in multiple choice and the rest in short answer questions.
They recommend you use the textbook from 1A, however it is possible to only use the handouts they give, otherwise resources outside of Moodle are quite scarce. Also there aren't any current lecture recordings, or maybe I'm not looking hard enough.
« Last Edit: December 07, 2018, 10:22:44 pm by MisterNeo »
HSC 2017 | ATAR: 95.60
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Bachelor of Commerce with Bachelor of Science (Computer Science) at UNSW

RuiAce

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Re: UNSW Course Reviews
« Reply #80 on: December 07, 2018, 09:42:47 pm »
+6
Subject Code/Name: COMP1521 - Computer Systems Fundamentals

Contact Hours: 2 x 2hr lecture, (1hr tutorial followed by 2hr laboratory)

Assumed Knowledge: COMP1511 is the sole prerequisite and is sufficient.

Assessment:
- 10% spread across 6 quizzes
- 9% assignment on assembly code
- 11% assignment on C
- 10% spread across labs
- 60% final exam

Lecture Recordings? Yes

Notes/Materials Available: Lecture slides on webcms3 - seemed sufficient

Textbook: None prescribed. Recommended was "Computer Systems: A Programmer's Perspective , by Randal E. Bryant and David R. O'Hallaron, Prentice-Hall" but I never had to use it.

Lecturer(s): Dr John Shepherd

Year & Semester/Trimester of completion: 18s2

Difficulty: 3/5

Overall Rating: 4/5 

Your Mark/Grade: 94 HD

Comments:
This course is one of the follow-ups to COMP1511, generally taken in the next semester (but can be delayed a fair bit for students not studying CompEng or not considering OS later on).

There are two halves to this course in my opinion. The first half essentially focused on what was going on within/inside the computer when programs are being executed - hence the discussion on assembly. The assembly language used was based off the MIPS architecture (although we used the SPIM simulator when writing up MIPS code), Conceptually it wasn't really hard understanding MIPS - all we had to do was convert C code into it, but it can be quite tedious. The MIPS assignment was straightforward but certainly time consuming and not something that could easily be winged. (Most people understood MIPS well enough as required by the course towards the end of the semester. but it may have caused difficulty during the learning phase.)

But it wasn't just assembly, like MIPS only lasted 2.5 weeks or so. There's also a slightly more in depth discussion with memory management and also the introduction of bit fields/unions. All of that stuff though I think I just rote learnt and took for granted.

The second half presented all the systems - we looked at the Unix file systems and tools and techniques that software/hardware developers used (e.g. sockets, concurrency). Moral of the story with all of that - know how to use the man pages. The manual is a lifesaver for this course (in the labs, assignments and for the exam).

For me, I felt having done COMP2521 (which I'd say was harder) in advance and coming back down to here did help. That course was about things you can do with your code (thinking like a computer scientist) whereas this actually explains all the behind-the-scenes stuff about the computer itself. But most people either do this course first, or take it concurrently with COMP2521 which is fair enough in my opinion. The stuff was pretty cool and more often than not seemed to make sense. (Although, I got VERY lost towards the end with sockets.)