UNSW Course Reviews

[anomalous]

Subject Code/Name: COMP4141 - Theory of Computation

Contact Hours:
- 2x 2 hour lectures
- 1x 1 hour tutorial

Assumed Knowledge: MATH1081, and either COMP1927 or COMP2521.

Be warned that the jump from something like 2521 to this is quite significant. If you wanted to be the most prepared, it would probably help if you did COMP3121/3821 beforehand, since you are at least introduced to the kind of problem solving you do in this course - make sure to pay attention to reductions in particular.

Assessment:
- 4x written assignments, worth 50% of your course mark in total (each equally weighed at 12.5%)
- final exam, worth the remaining 50% of your course mark

Lecture Recordings? Yes, screen and voice recorded. Tutorials, on the other hand, were actively discouraged from being recorded, so you either had to turn up to those or miss out.

Notes/Materials Available: There is a weekly set of tutorial questions, but solutions were very scarce - proper solutions to all were released < 24 hours before the exam started, but informal scratch solutions were given by one of the tutors a few days earlier at least.

Textbook: “Introduction to the Theory of Computation” by Sipser is the primary resource for this course and its analogues at other universities, and many references are made to it in lectures. If you really want more reading material though, then you can also try “Introduction to Automata Theory, Languages, and Computation” by Hopcroft, Motwani and Ullman. This book gets occasional references in lectures.

Lecturer(s): Dr. Paul Hunter

Year & Trimester of completion: 21T1

Difficulty: 5/5

Overall Rating: 3.5/5

Your Mark/Grade: 94 HD

Comments:
This is a really nice course, and it’s very interesting if you want to learn more about the fundamentals of how we define and analyse computation in its purest forms. The first half of the course answers the question “how can we model computation?” by looking at various types of languages and their corresponding machines. After these models have been sufficiently established, the second half of the course switches gears in order to answer the question “how can we properly analyse computation?”, by introducing formally the notion of resource-bounded computation and examining many complexity classes that generalise/extend the two most famous classes, P and NP. In some sense it’s a bit of a crime that people studying CS don’t have to do this course or anything similar: computation is the literal basis of the field, yet I think many would struggle to explain what exactly it means when asked. This is an essential for the theoretically-minded CSE student, or anyone who really likes mathematical COMP courses.

There is a price to pay for all of this fun, though - this is quite a hard course. There is no programming to speak of (although we did get to write Turing machines to be run on an online simulator, which is about as close as it gets), so if you struggle with some of the more CS-style math (think MATH1081), proving things and particularly at coming up with constructions, then it’s likely that you’re going to struggle to keep up with assessment tasks.

While this has been one of my favourites, what drags down my rating of the course in terms of the quality of experience is timing issues. Unfortunately, it seems like Paul was dealing with some personal issues on the side of lecturing this term, so there were quite a few instances of delays. A lot of the assignments were released a few days later than anticipated, we didn’t get a lot of the exam prep stuff promised (we ended up getting tutorial and assignment solutions, but none of the past assignment/exam questions) and we didn’t receive any marks back for the assignments until after the final exam. It’s a shame, because if he was a bit more prompt with things, or was at least up front that he was busy and couldn’t get them to us, this course would be an easy 5/5. I hope in future offerings he’s a bit more timely with releasing things.

[anomalous]

Subject Code/Name: COMP3131 - Programming Languages and Compilers

Contact Hours:
- 2x 2 hour lectures
- 1x 1 hour tutorial

Assumed Knowledge: The only prerequisite is COMP2511. All of the assignments are written in Java, and some OOP design patterns (notably, the visitor pattern) are used.

Assessment:
- 5x programming assignments, each with a specific weighting and contributing to an assignment mark out of 100
- final exam, contributing to an exam mark out of 100
- 1x bonus programming assignment, worth up to 5 (unconditionally-weighted) course marks

Your overall mark is the harmonic mean of your assignment and exam mark, plus however many marks you got for the bonus assignment. This means that you need to be consistent: if you aced the assignments but only scored a 65 in the exam, your final mark will come out to 78 before bonuses, which is probably lower than you’d otherwise expect compared to the arithmetic mean used by most other courses.

Lecture Recordings? Yes, they were live over Teams.

Notes/Materials Available: Decent; you get lecture slides and an almost weekly set of tutorial questions during the term, but we also ended up getting 4 past papers (albeit from about 20 years ago; still relevant, mind you) to help with exam revision.

Textbook: “Compilers: Principles, Techniques and Tools” by Aho, Lam, Sethi and Ullman is recommended. Jingling often refers to it as the "purple dragon book" due to its cover.

Lecturer(s): Scientia Prof. Jingling Xue

Year & Trimester of completion: 21T1

Difficulty: 3/5

Overall Rating: 4/5

Your Mark/Grade: 100 HD

Comments:
This is a course primarily focused on the frontend components of a compiler, i.e. the phases from reading in a source file to producing the first iteration of executable code for the program.  If you’re going into it to learn about things like compiler optimisations, then you might want to reconsider. You’ll spend most of your time learning about parsing programs and extracting meaning from them, both theoretically (via some automata and language theory) and practically (via scanner generators and things of that sort). You cover the process of how a program is turned into an abstract syntax tree, and then how the AST is used in order to check that it is semantically correct and then to finally generate executable code for that program. There are components of compilers past this point, but like I said, they aren’t within the scope of this course (or any other undergraduate course at UNSW anymore, it seems).

The heart of this course is the set of 5 assignments, with (almost) each one following on from the last by getting you to build a phase of a compiler for a simple variant of C. This is all done in Java, so you need to be confident with extending a given Java codebase, although specific OOP knowledge isn’t really needed outside of the basics and being vaguely familiar with how the visitor pattern works. You should also be comfortable dealing with a codebase that is a bit of a mess design-wise, as the starter code is quite questionable in that regard. A considerable amount of the lecture time is dedicated to talking about these assignments too, so in addition to the per-assignment FAQs and other documentation, you’ve got everything you need really, and they shouldn’t be too rough. Having imperfect solutions to the previous parts won’t hamper you too much, since compiled reference solution files are given so that the supporting code can at least work for you in subsequent assignments. However, if you do want to try and fix your code beforehand, Jingling always has assignment marks out within a few hours of submission, which is really quite an impressive logistical feat, and also very good for the students.

As an aside: this course pairs quite well with COMP4141 for the first few weeks. Just be careful of the workload later on in the term.

[Sdkkkt]

Subject Code/Name: MATH2871 - Data Management for Statistical Analysis

Contact Hours: 3 Hours of Lectures, 1 Hour of Laboratory/Tutorial

Assumed Knowledge: MATH1041 or ECON1203 or ECON2292 or PSYC2001 or MATH1231 or DPST1014 or MATH1241 or MATH1251 or equivalent.

Assessment: 4 online quizzes weighted 20%, 1 group assignment weighted 20%. The final exam is weighted 60%.

Notes/Materials Available: SAS notes provided in PowerPoint format available only on Moodle. However, Google is a great resource especially the SAS Documentation website. Tutorial/Lab questions and answers are also provided on Moodle. SAS practice multiple choice questions and answers are also provided.

Textbook: No prescribed textbook.

Lecturer(s): Dr Leung Chan

Year & Trimester of completion: 2021/2

Difficulty: 1/5

Overall Rating: 1.5/5

Your Mark/Grade: 85 HD

Comments:

This course is simply a database management course in SAS which teaches you the absolute basics of SAS and peaks at loops, arrays and merging data sets. Simple Excel 'spreadsheeting' is also used towards the end of the course but the bulk of the course is in SAS 9.4 (available through Citrix). Even though there are prerequisites, you won't use any mathematics and statistical knowledge in this course. Content is quite heavy and may be confusing to understand if this is your first time programming but don't worry you will definitely get the hang of SAS very quickly. If you have used data base management software then MATH2871 will be easier.

The 4 online quizzes are in Moodle format (~ 10 questions) and are spread throughout the term. The quizzes were all based off the lecture slides. The group assignment is a coding one and the tuts/lab work will be beneficial. The final exam is a bit tough and time consuming, so make sure you know your SAS well (do the tut/lab work, and practice your SAS MCQs thoroughly).

Unfortunately with the course, Dr Leung Chan was the main complaint from peers. His accent is extremely hard to understand especially when his lectures are just him reading off the SAS lecture slides. He also runs some of the tutorials too. I felt that it became a self-learn/self-teach class because of this, so be aware. A positive though is that he was responsive on the forum and there weren't any admin issues throughout the course! Also please NOTE lectures and tutorials are not recorded due to SAS restrictions. Due to Dr Leung Chan and non-recordings, I felt the course was dragged down to a 1.5/5.
 

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Subject Code/Name: MATH3361 - Stochastic Differential Equations / MATH5361 - Stochastic Differential Equations (postgraduate equivalent)

Contact Hours:
- 1 x 2 hour live lecture.
- 1 x 1 hour tutorial.
- 1 x 1 hour lab.

Assumed Knowledge:
The official pre-requisite is MATH2011 or MATH2111 or MATH2018 (DN) or MATH2019 (DN) or MATH2069 (DN) and MATH2801 or MATH2901 or MATH2089 (DN) or MATH2099 (DN).

Assessment:
- 2 x class tests (7.5% each, totalling 15%)
- Lab test (15%)
- Assignment (20%)
- Final exam (50%).

Lecture Recordings? Yes.

Notes/Materials Available: Lecture slides are sufficient.

Textbook:
None prescribed.

Lecturer(s):
- Lecturer: Prof. Thanh Tran

Year & Trimester of completion: 2021 Term 1

Difficulty: 3/5

Overall Rating: 4.5/5

Your Mark/Grade: 88 (HD).

Comments:
This course made me appreciate the things we took for granted in MATH2901. The course begins with some discussions on stochastic analysis (which is probably the most pure component of the course) before the discussion on stochastic differential equations begin. If you're a stats major looking for an elective, this course is definitely something to consider. For the bulk of the course, however, it places a heavy emphasis on the differential equations aspect, as well as some discussions on some numerical approximations using Euler-Maruyama and Milstein.

[Opengangs]

Subject Code/Name: MATH3711 - Higher Algebra / MATH5706 - Modern Algebra (postgraduate equivalent)

Contact Hours:
- 2 x 2 hour live lecture.
- 1 x 1 hour tutorial.

Assumed Knowledge:
The official pre-requisite is 12 UOC of Level 2 Mathematics with an average mark of at least 70, including MATH2601 or MATH2501 (CR).

Assessment:
- Mid-term exam (25%)
- Assignment (15%)
- Exam (60%)

Lecture Recordings? Yes.

Notes/Materials Available: Lecture slides are sufficient.

Textbook:
None prescribed.

Recommended textbooks:
Artin, Algebra (I used this textbook and it was pretty alright. Nothing too spectacular.)
Armstrong, Groups and Symmetry
Rotman, A first course in abstract algebra
Herstein, Topics in Algebra
Jacobson, Basic Algebra I
Stillwell, Elements of Algebra
Lang, Algebra
Lederman, Weir, An Introduction to Group Theory

Lecturer(s):
- Lecturer: Dr. Mircea Voineagu

Year & Trimester of completion: 2021 Term 1

Difficulty: 3.5/5

Overall Rating: 4/5

Your Mark/Grade: 89 (HD).

Comments:
This is your standard abstract algebra course. The course is split primarily into three parts: group theory, ring theory, and field theory. The latter two are combined as one since they play very similar roles. For the bulk of the course, you will be discussing group theory, properties of groups, and important theorems that surround them. You will have had brief stints with groups in MATH2601 and MATH2701, so the properties of groups should be no surprise to you. The difficulty of the course begins to ramp up as you dive deeper into these theorems. The second half of the course talks about ring and field theory, two more important algebraic structures that are built from the study of groups.

I went into the course thinking this would be quite dry but actually really ended up enjoying it. The course content was interesting enough to keep me from falling asleep at 9am lectures. The pacing of the course is quite fast so it's in your best interest to keep up to date to prevent last minute cramming. But it's a course worth doing if you enjoyed the group theory component in MATH2601 (not like you have a choice if you choose to major in pure anyways).

[HelpICantThinkOfAName]

Time to kick off T2 2021!

Subject Code/Name: ECON3101 - Markets and Frictions (Microeconomics 3)

Contact Hours:  2 x 1.5 hour lecture per week. 1 x 1.5 hour tutorial per week.

Assumed Knowledge: ECON2101. Be very comfortable with micro 2 and calculus. I'd also recommend having taken ECON2112 and one other third-year micro course before this (behavioural, organisational, or IO)

Assessment:

4x10% homework assignments. Nothing too difficult, just a lot of algebra

4x5% journal entries. These are short 300-500 word journal entries similar to ECON1401, but with more engagement with the course. You can't say "in week 1 we studied x and I found this interesting because...", you have to talk about where the topics discussed fail in the real world, or connect it to some other course, or even to literature (apparently someone related consumer theory to Dostoevsky???)

40% final exam. Nothing too difficult, about the same level of the problems discussed in the tutorials, not quite as hard as the homework quesitons.

Lecture Recordings?  Yes.

Notes/Materials Available:  Full slides. Would recommend having a copy of the Varian textbook handy, always nice to have a second explanation of these topics.

Lecturer: Gautam Bose, 3.5/5. Fairly decent lecturer, with good explanations of the topics. I feel that the lectures towards the end of the course would've benefited from more time.

Year & Trimester of completion: 2021/T2

Difficulty: 3.75/5 for the whole course

Overall Rating:  3/5.

Your Mark/Grade: 86 HD

Comments: This course is a marked improvement over micro 2, but also suffers from some similar failings. Namely the lack of examples done in lectures and tutorials. This time the lectures went much more in-depth with the material than micro 2 did, so it was never really a problem until later into the term, where it would've been nice to have some more concrete mathematics shown. If you like micro or are planning on taking economics honours, this is a course that you can't miss.

[fun_jirachi]

Subject Code/Name: MATH2901 - Higher Theory of Statistics

Contact Hours:
2 x 2hr + 1 x 1hr lectures
1 x 1hr tutorial

Assumed Knowledge:
MATH1231 or MATH1241 or MATH1251 or DPST1014 (or, in program 3653, MATH1131 or MATH1141

Assessment:
5% Mobius Quiz
15% Assignment
20% Midterm
60% Final Exam

Lecture Recordings?
Yes

Notes/Materials Available:
Lecture slides, R manual, course formula sheet, tutorial questions are provided and sufficient

Textbook:
Above materials

Lecturer(s):
Dr Donna Mary Salopek

Year & Trimester of completion:
21T2

Difficulty:
3/5

Overall Rating:
0.5/5 (down from pre-exam 2 because of the exam)

Your Mark/Grade:
80 DN

Comments:
Wanted to do this first while I'm still a little bit mad, don't want to waste energy later getting mad again.

If a course's saving grace and only salvageable positive is the course content, you're doing something wrong. It's a prized asset that should ideally make courses great. Unfortunately, that was the case for this particular course. I want to stick to slagging off the course but it's really tough to do without putting my toe across the line, so I'm going to stick to two main issues:

Problem 1: Assessments
A potential final with a portion on Mobius? That angers me and a lot of the students (from the little I have heard). (also the reason why the 2/5 rating remains tentative). This also comes after the uproar surrounding the midterm on Mobius. While some of my problems with the midterm include some self-sabotage marks wise, it's tough to objectively ignore the associated issues with a) demonstration of knowledge and b) results distribution.

Problem 1a) Mobius is such a reductive platform that is good only for quizzes that were basically free marks anyway. For actual demonstration of what you've learned, it's genuine garbage. For maths, you need to show thought processes and working out, which was only available on one question in the midterm. Hearing that it will likely compose a part of the final pisses me off a lot. Again, the platform is reductive as hell and marks a huge departure from in person exams. The best way to mimic these and allow to you use the skills taught in each course is to have moodle submissions of pictures of your work (which literally every other course uses; I've done this in every maths course after math1a/1b (admittedly only two) and it is so much better).

Problem 1b) This is somehow worse than problem 1a). We were told we weren't allowed to review our midterm because a) questions were going to be reused in later course offerings and b) it wouldn't have helped us learn anyway. If there's anything I've learned in general from school, it's that everyone learns differently. Not even getting the choice to review our midterm and instead having the choice made for us was nothing short of bullshit. I don't even know where I screwed up (and I screwed up heavily, as mentioned). I wanted to improve by looking at my mistakes and making sure I document them so as not to make them again, this is impossible because of the aforementioned situation. It's also telling that I'm still fuming about this three weeks after the midterm.

Problem 2: Exam practice
I am also still fuming about the lack of exam practice usually provided by other mathematics courses. While I get that the onus is on the student to prepare, I don't think it equates to poring over lecture slides and a completing a painfully limited set of tutorial problems which you can only do so many times. The wishy-washy excuse for not being given past midterms and finals was 'the questions will be different, it won't be relevant anyway'. Again, the part that irks me is that the student was not allowed to make the choice as to whether this practice would be beneficial or not (which personally, it is, practicing concepts makes them click more intuitively). Applying concepts proves you've learned them, when little or no such opportunity presents itself it's hard to justify to yourself that you've learned anything at all. It's just been a frustrating ride overall, only the cool content has really salvaged anything, at least I can say it definitely had me paying attention.

A lot of these issues can be attributed to the administration of the course, so hopefully the course itself remains unsullied if the administration improves. There is plenty more I can delve into but won't because it's not pertinent to the course itself and rather the way it was taught. Like many courses I've reviewed previously, stick around just for the content, but you'll really have to stick it out. Unfortunately it's a key course for mathematics, so I can't not recommend it - but if it was avoidable I wouldn't take it in its current state.

Post-final edit: The only creditable aspect of the course was that the final had a better format than expected ie. file submissions.  That is the only part which I will give any credit for (the relevant pre-exam criticism can be rescinded). It should still be noted that Mobius file submissions have limitations on filenames and is in general clunkier than a Moodle submission (personal preference only). However, the organisation of the final was still disastrous. Key edits to the paper (for example a probability distribution function that wasn't actually a probability distribution function) came frustratingly late and forced me (and I presume at least a few other students, if not more) to lose time both fixing my answers to reflect the edits and on other questions since I had to fix my answers. It's telling that I wasn't surprised by this, nor the fact that there were no immediate concrete concessions made to alleviate potential losses. I quote: 'No one will be penalized if you did not see the corrections', which sounds to me like errors that reflected the original paper on the questions that had typos were not penalised, but other questions would be; hence, the second point I make (about losing time on other questions by altering answers to reflect corrections) was not accounted for by any publicly announced concession. While the exam was more than doable, the organisation of it was essentially a microcosm of the course as whole.

[fun_jirachi]

Subject Code/Name: MATH2601 - Higher Linear Algebra

Contact Hours:
1 x 1 hr tutorial
Prerecorded lectures ~ 3-4 hours in length weekly.

Assumed Knowledge:
MATH1231 or MATH1241 or MATH1251 or DPST1014, each with a mark of 70 or higher

Assessment:
2 x 6% Mobius Quizzes
2 x 15% Class Tests
1 x 10% Assignment
48% Final

Lecture Recordings?
Yes

Notes/Materials Available:
Lecture slides, past class tests, practice tutorial set, past finals, weekly tutorial problems

Textbook:
None, the above is sufficient

Lecturer(s):
Dr John Steele

Year & Trimester of completion:
21T2

Difficulty:
3/5

Overall Rating:
3/5

Your Mark/Grade:
83 DN

Comments:
It's really hard to screw up a course like this, which makes the above review somewhat more scathing. The assessment structure was exactly what you'd like (can't complain really, any Mobius element was supposed to be free marks, which works for most people), class tests actually testing people. The course content makes you go 'oh, that's pretty cool' occasionally but often regresses to a love/hate dichotomy. In a lot of ways, I could get away with copy-pasting the first paragraph of review from last term on MATH2111. Also, while the prerecorded lectures allowed for flexibility, they were often really dry. There's not really a lot to say since this is a compulsory course and was definitely a stock standard maths course. Special shoutout to David Angell who took my tutorial, absolute treasure to UNSW maths and stats and made the experience that much better

[fun_jirachi]

Subject Code/Name: COMP3121 - Algorithms and Programming Techniques

Contact Hours:
2 x 2hr lectures

Assumed Knowledge:
Prerequisite: COMP1927 or COMP2521

Assessment:
4 x 10% Assignments
60% Final

Lecture Recordings?
Yes

Notes/Materials Available:
Lecture notes, slides, exercise set for each topic (with solutions)

Textbook:
Kleinberg and Tardos: Algorithm Design
Cormen, Leiserson, Rivest and Stein: Introduction to Algorithms

Lecturer(s):
Dr Aleksandar Ignjatovic

Year & Trimester of completion:
21T2

Difficulty:
2.5/5

Overall Rating:
4.5/5

Your Mark/Grade:
93 HD

Comments:
Will start by praising the lecturer who made lectures worth showing up to - personable, clear, appropriate amounts of detail for literally everything.

As for the course itself, it was a great course! Probably one of the most useful courses I've taken to date. Course content and the course itself were really well set out and I don't recall ever being lost. While I can have my complaints about the course forum, that's about where the complaints stop. The assessments were relatively chill and the workload was comparatively low; any stress I had with this subject was due to other subjects having assessment due dates converging, and was not as a result of this subject alone. What feedback I got on assignments was valuable, and the tutorial questions covered most variations of concepts taught, with good solutions that explained pretty much everything. It's impossible not to come away from this course having learned nothing of value, and having not enjoyed it (with the caveat of some engagement). While some students can claim to have been confused at one point with the content, the administration of the course is something I feel cannot be complained about, everything ran smoothly. Any enjoyer of COMP2521 should definitely take this course, as should any other interested party

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Subject Code/Name: COMP3153 - Algorithmic Verification / COMP9153- Algorithmic Verification (postgraduate equivalent)

Contact Hours:
- 2 x 2 hour live lecture.
- 1 x 1 hour tutorial.

Assumed Knowledge:
The official pre-requisite is MATH1081. Recommended courses to take before COMP3153: COMP2111 and/or COMP4141.

Assessment:
Original assessment schedule (see comments):
- 4 x assignments (2 x 15%, 2 x 10%)
- Final exam (50%)

Lecture Recordings? Yes.

Notes/Materials Available: Lecture slides are sufficient.

Textbook:
A list of texts can be found at the end of each week's slides for further reading.

Lecturer(s):
- Lecturer: Dr. Paul Hunter

Year & Trimester of completion: 2021, Term 2

Difficulty: 3/5

Overall Rating: 2.5/5

Your Mark/Grade: 85 HD.

Comments:
In short, the course was a huge interest booster marred by poor management by the administration team.

This course serves to be the more practical side of theoretical computer science course. The original plan was to be introduced to a range of verification model checkers, such as SPIN/Promela, nuSMV, and SLAM/CBMC which helps to verify algorithms to solve different tasks (including satisfiability and LTL model checking). The theory of the course introduces students to concepts in modern day static analysis and logic in time (temporal logic), which is crucial to algorithmic verification (hence, the name of the course).

The theory was interesting and has definitely been one of the better computer science courses as far as the content goes. Although there wasn't a lot of mathematical concepts, I found myself enjoying the study on topology of behaviour spaces and found that engaging. The assignments weren't too hard and it definitely helped grasp the concepts of the course a lot better.

However, this course has been a nightmare to manage and study for. Assignments were released late, tutorial problem solutions weren't released at all (or at least, they were released extremely late). As a result, assignment marks were released extremely late. Assignment 1 marks were released a few hours before the extended census date - even then, I had to email Paul himself to see where he's up to in terms of marks. To give a taste for how late assignments were released, assignment 3 was meant to be released in week 8. However, delays happened and it was released around the end of week 10 and due on Wednesday of STUVAC week. I am sympathetic to the fact that this is Paul's first time lecturing this course (after Liam's departure) and I have no doubts that the next few offerings will be better.

With the way that assessments have been laid out, there has been a massive overhaul in the distribution of assessments. Assignment 4 (still yet to be released as of writing this review) is now optional, while the previous three assignments have been scaled accordingly. If you were to do assignment 4, then Paul will consider the final marks with and without assignment 4 and take the maximum of the two.

[HelpICantThinkOfAName]

Subject Code/Name: ECON3106 - Politics and Economics

Contact Hours:  2 x 1.5 hour lecture per week. 1 x 1.5 hour tutorial per week.

Assumed Knowledge: ECON2101. I'd also say take 2112 before this as well.

Assessment:

6 x 3% problem sets.  3 of these involved deriving equilibrium conditions for some setup, the other 3 were responding to empirical papers. I liked these, they weren't super intensive, but you did have to understand the material to do well on them.

16 x 1% online quizzes. 2 of these were due for each week of lectures. Just short multiple-choice quizzes (4-5 questions each), nothing very intense. You can repeat as many times as necessary. Free marks.

4 x 5% discussions. These were in-tutorial discussions about research papers that we were given a week to read. This is the weirdest part of the course for me, it seemed very difficult to actually get the marks for this, 3-4 contributions per discussion were needed from my experience, but when you have 30 people in an online tutorial it gets very messy very quickly - you're forced to gun for the marks early on so that nobody steals your idea and so you can actually get a word in.

46% final exam. This wasn't very difficult overall. 2.5 hours to do 2 short math questions, 2 short responses, and 3 multiple-choice questions. The final exam covered (at most) 10% of the course, and didn't take anywhere near 2.5 hours to complete. There were strange word limit requirements though, with one saying that your mathematical reasoning had to be strictly below 20 words.

Lecture Recordings? Pre-recorded lectures, four per week. This is one of the few times where I have felt that the pre-recorded lectures were superior to having live lectures, mainly due to the lecturers.

Notes/Materials Available:  Full slides.

Lecturer: Gabriele Gratton, 5/5. His lectures are always engaging and he was super helpful throughout the term. If you have the opportunity to take a course under Gabriele, take it.

Federico Masera, 4/5. Not quite at Gabriele's level, but close. A big improvement (in my opinion) over 2206.

Year & Trimester of completion: 2021/T2

Difficulty: 2/5.

Overall Rating:  5/5.

Your Mark/Grade: 82 DN.

Comments: This is such a great course. I wasn't very convinced early on, but the topics flow nicely from one to another, individual preferences lead to social choices, then to lobbying and corruption, political advertising, and conflict. The course cleverly integrates empirical data throughout to reinforce what the theory is saying, and closes with a lecture devoted to exploring empirical data. It's well structured, well-paced, well taught, and well assessed. If you're looking for a third-year economics course that isn't super intense, this is your new best friend.

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Subject Code/Name: MATH1041 - Statistics for Life Sciences

Contact Hours:
- 2 x 2 hour live lecture.
- 1 x 1 hour tutorial.

Assumed Knowledge:
No assumed knowledge is required for the course.

Assessment:
- 9 x online tutorials on Mobius (10% altogether)
- 1 x midterm (15%)
- 1 x assignment (15%)
- Final exam (60%)

Lecture Recordings? Yes.

Notes/Materials Available: Lecture slides are sufficient.

Textbook:
The recommended textbook is Introduction to the Practices of Statistics.

Lecturer(s):
- Lecturers: Dr. Nicole Mealing and Dr. Laure Helme-Guizon

Year & Trimester of completion: 2021, Term 2

Difficulty: 1.5/5 (the average MATH1041 student, though, may feel the difficulty is about 3-3.5/5)

Overall Rating: 2/5

Your Mark/Grade: 85 HD.

Comments:
I did this course purely because it was going to be a WAM boost but I'm disheartened by a few things from this course. My biggest complaint is the assignment. I'll get to that in a bit.

The course serves to be one of two math-intensive courses for people doing a life science degree, the other being MATH1031 which is more akin to the content you find on MATH1131. MATH1041 is an introductory course on statistical practices for people who may not want to do any more mathematics courses beyond this course.

For the most part, the course was just revision from when I took MATH2901 and a lot of the content felt very familiar to me. You learn about the basic probability theory principles as well as the basic statistical inferences that permeate any statistics major (confidence interval, hypothesis testing, inferences about the mean). The midterm was also really nice, we were given enough practice to perform well in the test and it felt like a nice 15% to get. The tutorials felt very dry and I felt that the tutors didn't want to be there (or at least I got that energy from them), which made me not want to go to the tutorials.

Then came the assignment. The assignment was convoluted and long, I felt like I was answering the same question 3 or so times. I was confused a lot of the times because the questions were worded awkwardly and left a lot of room for interpretation. When anyone tried to clarify a question, the answers were always the same: You're being assessed on it. When addressing a missing unit in the assignment, the lecturers didn't bother making a public announcement. Instead they simply left a comment in the thread that was so difficult to find because it was so hidden among the other questions that arose from the assignment. As a result, I had to redo one of the graphs because I realised oh there WAS a unit attached. I felt that, if we got marked down for it, there would be some major complaints. The marking was a bit controversial as well. A lot of assumptions that were not made clear from the assignment specifications were vital to how you were supposed to answer. This made the assignment even more frustrating to do. There were parts of the sample answer that I was not happy with and it felt like the markers were rushing to get marking done. Losing full marks for small errors should never be permitted.

[HelpICantThinkOfAName]

Subject Code/Name: MATH3311/MATH5335 - Mathematical Computing for Finance/Computational Methods for Finance

Contact Hours:  2 x 1.5 hour lecture per week. 1 x 1.5 hour tutorial per week.

Assumed Knowledge:  (MATH2121 or MATH2221 or MATH2111) and (MATH2501 or MATH2601) and (MATH2801 or MATH2901 or MATH2871). Be very comfortable with all of these. I'd go as far as to say take all of the core second-year math subjects before taking this (yes, even complex analysis).

Also, take 2301. I found it helpful to have some decent background knowledge in Matlab, although there's no need to already know Matlab. There are free and accessible resources to help you learn, and it's quite simple to pick up if you're already decently versed in another programming language, or if you're learning from scratch!

Assessment:

4x5% Assignments. These weren't too difficult for the most part, mainly being able to translate mathematical logic into Matlab efficiently. There are a few times where you'll want to bang your head into a desk, but as long as you know a couple of tricks (such as not always needing to store an entire matrix, but just one vector that you can play around with for some recursive fun), you shouldn't have too much trouble with it.

20% Matlab Test. This was a bit scary in the leadup, but as long as you just grind out the lab work and past papers you'll smash this.

60% Final exam. Very little preparatory material for this, only past papers. There's no 30-page problem set for this course (unlike every other math course I've taken). The only stuff you're given are weekly self-study questions taken from past papers, so what'll happen is that you finish up the term and realise that there are no new problems for you to study. This made me feel very underprepared going into the final, and I had gotten near full marks on the assignments and problem sets. The final for MATH3311 is shared for MATH5335, so there may be some scaling up for 3311 students.

EDIT: I just finished the exam. What a shocking paper. There were large parts of that paper that we never once covered in lectures, never touched on in labs, and never appeared in past papers, and it certainly didn't come from the textbook or any of the prerequisite courses. I can only pray that we're scaled like hell, because I was only able to answer 25% of that exam with some confidence.

Lecture Recordings? Pre-recorded lectures, accessible from the start of the course. They go in-depth with all the topics, and even into some non-assessable topics if you're super keen.

Notes/Materials Available:  Full slides. As I said above, there are no problem sets for you throughout this course, so you're basically relying on remembering everything you learnt in second year. Some extra resources (even just some refresher questions) would've been helpful.

Lecturers:

Professor Josef Dick, 3.5/5.  He was alright, explained things well, managed to keep my attention.

Dr Leung Chan, 3/5. A bit difficult to understand at times, but alright for labs.

Year & Trimester of completion: 2021/T2

Difficulty: 4/5.

Overall Rating:  2/5.

Your Mark/Grade: 67 CR (seems like the final exam was heavily scaled)

Comments: For a course called Mathematical Computing for Finance, I expected a lot more of the finance side to come into play. It wasn't until week 7 that we first talked about Black Scholes, and that was only in the context of non-linear equations. The course isn't super focused on finance, nor solving problems related to finance.

The first half of the course is your typical "here's Matlab, here's what you can do, here's why it's a terrible idea to use Matlab". The second half covers numerical integration, random numbers, simulations, and PDE's, with a few equations used in finance dotted throughout. I would've much preferred that the course have a focus on financial applications from the start, or at least have problems that help you to see the connection between what you're learning and what is used by financial analysts. Because of this I never felt that the course went below a superficial skim of financial computing. I still learned a lot about Matlab from this course, and if it weren't for catfishing us by calling it computing for finance I'd easily give it a 3.5 or a 4. It just feels like what I learned wasn't quite what I signed up for, which is a shame.

That's a wrap on my math degree though! What a journey! From failing extension math in year 11 and being told that a degree involving lots of maths wouldn't be a good idea, to ticking off all the requirements for a mathematics degree! Excited to finish up my economics courses next term, and then (hopefully) start my economics honours next year!

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Time to put my remaining two courses up.
Subject Code/Name: MATH2400 - Finite Mathematics

Contact Hours:
- 2 x 2 hour live lecture.
- 1 x 1 hour live lecture.
- 1 x 1 hour tutorial.

Assumed Knowledge:
Pre-requisites are either MATH1081 or MATH1231/1241/1251 or DPST1014. Highly recommend doing this course after or with MATH1081.

Assessment:
- 2 x class tests (20% each)
- Final exam (60%)

Lecture Recordings? Yes.

Notes/Materials Available: Lecture slides are sufficient.

Textbook:
No textbooks for the course.

Lecturer(s):
- Lecturer: Prof. Igor Shparlinski.

Year & Trimester of completion: 2021, Term 2

Difficulty: 2/5 (although having done MATH3711 and MATH3411 beforehand played a major role in this difficulty score; it would probably be around 3.5-4/5 for someone coming into the course with only MATH1081 or MATH1231)

Overall Rating: 4.5/5

Your Mark/Grade: 97 HD.

Comments:
I didn't have to do this course but it ended up being one of my favourite courses this term. The course has given me a new profound appreciation towards MATH3711. The course goes through a lot of the abstract algebra concepts you would find in a course like MATH3711 without getting bogged down in the details of proofs. (Don't worry, there are proofs in the course but the focus is more on the applications of the concepts). Think of it as an introductory course to algebra and not the algebra you find in high school!

The course is designed to be taken alongside MATH2859 (hence, the 3UOC instead of the normal 6UOC). Don't get confused between 3UOC and the workload however! The workload is about the same as a normal 6UOC course and you should treat it as such.

The first half of the course is a revision of the number theory components of MATH1081. You revisit concepts such as the Euclidean Algorithm and divisibility. You then cover other algebraic structures such as groups, (commutative) rings and fields which become an integral part of the second half of the course (coding and information theory). So if you enjoyed modular arithmetic in MATH1081, this is a great follow up course for you to do! On the other hand, if you enjoyed MATH3411 and want to do a bit more on coding theory, then this course is also a great course for you!

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Subject Code/Name: MATH3611 - Higher Analysis / MATH5705 - Modern Analysis (postgraduate equivalent)

Contact Hours:
- 2 x 2 hour live lecture.
- 1 x 1 hour tutorial.

Assumed Knowledge:
Pre-requisites are 12 UOC of Level 2 Mathematics with an average mark of at least 70, including MATH2111 or MATH2011 (CR) or MATH2510 (CR), or permission from the Head of Department.

Assessment:
- 3 x assignments (10% for the first two, 20% for the main assignment)
- Final exam (60%)

Lecture Recordings? Yes.

Notes/Materials Available: Lecture slides are sufficient.

Textbook:
The recommended textbook is Introductory Real Analysis by Kolmogorov.

Lecturer(s):
- Lecturer: A/Prof. Pinhas Grossman

Year & Trimester of completion: 2021, Term 2

Difficulty: 3.5/5

Overall Rating: 4/5

Your Mark/Grade: 78 DN.

Comments:
Definitely the most challenging course out of the four courses I did this term, and it's not surprising why. This is one of three core courses for anyone planning to go into Pure Mathematics and it serves to be the more "calculus" heavy course out of the three. Essentially, this is a rigorous calculus class and you need to have a certain mathematical maturity to do well in the course. The lecturer doesn't cover many proofs but rather develops the intuition for what the proof should look like and it's your job to fill in the details, and it's a pretty nice system to have.

The course begins with a conceptual understanding of what it means for you to say "cardinality" of sets (in particular, infinite sets), covering topics such as countability and uncountability before diving into the first real topic of analysis -- metric spaces. You'll develop an understanding for abstracting away from the concrete (instead of talking about distance functions, we can talk about metrics of a space). The course ends with a fairly dense topic on compactness of topological spaces, a topological property that generalises the notion of boundedness and closed-ness.

In all, I found this to be a really interesting course and the lecturer does an excellent job at explaining these topics in a way that seems fluid and cohesive. If you're interested in pure mathematics and want to dive into some more calculus, then this is definitely a course for you.