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June 01, 2023, 02:30:30 am

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anomalous

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Re: UNSW Course Reviews
« Reply #270 on: September 02, 2021, 12:39:51 pm »
+4
Subject Code/Name: COMP3141 - Software System Design and Implementation

Contact Hours: Just 2x 2 hour lectures, but there is a split:
- One of them is a content lecture which introduces the course content for the week
- The other is a practice lecture, which covers the solution to the previous weekís programming exercise as well as reinforcing the material from the content lecture, usually with a focus on working through actual problems

Assumed Knowledge: Either COMP1927 or COMP2521.

Assessment:
- 2x programming assignments, worth 20% of your mark (10% each)
- 8x online quizzes, worth 10% of your mark (be warned, these are very difficult!)
- 6x weekly programming exercises, worth 20% of your final mark
- Final exam, worth 50% of your mark, with a hurdle of 40/100 in the exam to pass overall

Lecture Recordings? Yes, recorded and uploaded onto YouTube.

Notes/Materials Available: Not much, but what you do get is nice: a set of good lecture slides and some tutorial questions.

Textbook: No textbook required, but the following are recommended as Haskell references by the course if you're looking for something:
- Thinking Functionally with Haskell, by Richard Bird
- Haskell Programming From First Principles by Christopher Allen and Julie Moronuki
- Programming in Haskell by Graham Hutton
- Real World Haskell by Bryan O'Sullivan, Don Stewart, and John Goerzen
- Learn You a Haskell for Great Good! by Miran Lipovača

Additionally, the course content draws from Data Refinement: Model-Oriented Proof Methods and their Comparison by Kai Engelhardt and W.P. de Roever, but itís said that this text is not suited for undergraduates.

Lecturer(s): Dr. Christine Rizkallah and Curtis Millar (who have both now left UNSW)

Year & Trimester of completion: 21T2

Difficulty: 4.5/5 without functional programming experience, 3/5 if youíve done some before

Overall Rating: 5/5

Your Mark/Grade: 93 HD

Comments:
Most people treat this as ďthe Haskell courseĒ because there is a fair bit of Haskell programming, but thatís not its stated intention. Rather, it provides a perspective on how we can use ideas inspired by mathematical proof and reasoning to construct safe software: Haskell just so happens to be a good language for applying this theory. The true value of this course is in the appreciation it gives you for safety and reasoning about programs, which is a point that some people (typically the more applications-focused crowd, though I mean this in the nicest way) can miss, because a lot of the stuff in this course can come across as abstract nonsense. It really does force you to examine how you previously approached correctness and take a more principled approach to designing software, not only during the development process but also the testing process. On its own this is a very useful perspective to have, and makes this worthy of consideration as an elective for CS students (worth noting this is core for Software Engineering students, and I definitely agree with that).

If you havenít done functional programming before, this course will probably make you feel like youíre relearning programming, which is entirely normal. You donít have to write very much code at all in this course in terms of the number of lines needed to finish most tasks, but the tradeoff is that youíll be thinking much harder about each line than youíve probably ever done up until this point. To supplement all of this programming, there is some theory regarding types and the connection between programs and proofs, which is probably the coolest bit of the whole course (the surprise is ruined if youíve seen it before as I did though). Structural induction and natural deduction are also taught as it relates to that theory. While this is a bit of maths, donít worry - very few courses are necessary nor sufficient to have already covered it going into the course, so it gets taught from scratch.

Overall, quite a fun course if a bit tough at times. This is a must do if youíre interested in functional programming, since this course offers the most substantial introduction to the area at UNSW. If you like this course, consider following it up with COMP3161, which offers an analysis of programming language design (and particularly the design of functional programming languages).

RuiAce

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Re: UNSW Course Reviews
« Reply #271 on: September 02, 2021, 04:43:32 pm »
+2
Subject Code/Name: MATH5845 - Time Series

Contact Hours: 2 x 2hr lecture (no tutorials; some 'tute' questions covered in the lecture)

Assumed Knowledge: None explicitly stated, but just as with all level 3/5 statistics courses you should have foundation up to second year statistics (MATH2801/2901 level). Knowledge of linear models (MATH2831/2931) is highly recommended for one topic, but it only matters for that topic, and you only need to understand the linear model itself (don't worry about F-tests etc). MATH3801/3901/5901 not required,

Assessment:
- 1 x 15% Assignment
- 1 x 20% Assignment
- 5% Class participation
- 60% Final exam

Lecture Recordings? Yes

Notes/Materials Available: Detailed lecture notes and lecture scribbles are given. Excerpts from textbooks given.

Textbook:
- Shumway, R.H. and Stoffer, D.S. (2016) Time Series Analysis and Its Applications with R Examples, 4th edition, Springer-Verlag, New York
- P. J. Brockwell & R. A. Davis (2002), Introduction to Time Series and Forecasting, Second Edition, Springer-Verlag, New York.
They're both good reads, but not needed.

Lecturer(s): Dr. Zdravko Botev

Year & Trimester of completion: 21 T2

Difficulty: 4.5/5

Overall Rating: 4.5/5

Your Mark/Grade: 93 96 HD

Comments:
This is one of many postgraduate statistics courses. Recently, it has remained on a yearly offering.

Time series branches off from stochastic process. It is the analysis of data that is indexed by a time variable. Time is assumed discrete in time series, because in practice although the phenomena may be continuous, you only collect it at discrete time intervals. In practice your time series data can be quite long (collect data over lots of timestamps), but you only study the data set itself. There is no comparison between two time series in this course.

The first thing to mention is that this is a Zdravko course. He teaches you the theory. It's more appropriate to think of this course (at least presently) as Theory of Time Series. You'll be introduced autocovariance/autocorrelation, ARMA, spectral densities, etc. all from a mathematical standpoint. Of course, there are a couple questions that make you apply the theory to solving real problems/on real data sets, e.g. maximum likelihood of the ARMA parameters. For someone like me, this is exactly what I want. Yet somebody who only cares about applications may not be so interested.

The first half of the course introduces the mathematical background (including autocorrelation; quite surprisingly huge) needed for time series algorithm. The second half focuses on time series concepts, and develops the algorithms that typically get implemented for time series analysis.

Class participation is free marks - just contribute once (question OR answer) and you walk away with 5%. Quizzes are mostly free marks as well. Basically, the question bank gets released, and one question gets randomly selected for which you have to submit a response for. We had at least 1 week to prepare our answer for both the quizzes. The difficulty really comes from the final exam in my opinion (up till then, difficulty is something like 2.5/5).

In short, I just felt there was no time to answer everything. It was nice to know that out of the 4 questions given, we only needed to answer 3 such questions. Somehow, one of the three I picked was way too long. I remember submitting the exam with 26 or so seconds to spare; zero time to actually check my answers.

In terms of the coding, Zdravko supports at least Matlab, R, and Python. Choose any one of the three, and roll with it. (However, his live coding is in Matlab, because that's what he's more comfortable with.)

Despite being a theoretical course though, I would at least ask many postgrad students "why would you skip time series"? It's still pretty fundamental to know, in my opinion, as a working statistician. (Time series is also used in ML apparently, but I haven't investigated how.)
« Last Edit: December 16, 2021, 10:07:38 pm by RuiAce »

anomalous

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Re: UNSW Course Reviews
« Reply #272 on: September 02, 2021, 05:00:36 pm »
+3
I originally wasnít going to post this review since another poster gave an excellent summary, but I have been persuaded...

Subject Code/Name: MATH2901 - Higher Theory of Statistics

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

Assumed Knowledge: Formally, only one of MATH1231, MATH1241, MATH1251 or DPST1014 is required.

However, I would probably recommend you to have done MATH2011 or MATH2111 as well, not just because some of the content from it appears, but also because youíll benefit from the mathematical maturity.

Assessment:
- Quiz, worth 5%
- Midterm quiz, worth 20%
- Written assignment, worth 15%
- Final exam, worth 60%

Lecture Recordings? Yes, on Blackboard Collaborate.

Notes/Materials Available: Nothing too impressive here: some mediocre lecture slides that had typos in them here and there, some tutorial problems with solutions and some notes on how to use R (which were actually very good).

Thereís a very comprehensive set of course notes floating around from a previous lecturer of the same quality as the R notes, although you had to find these on your own as they werenít provided.

Textbook: Not required, but Introduction to Mathematical Statistics by Robert Hogg may be helpful.

Lecturer(s): Dr. Donna Mary Salopek

Year & Trimester of completion: 21T2

Difficulty: 4/5

Overall Rating: 0.5/5 - harsh, but unfortunately deserved in my eyes

Your Mark/Grade: 84 DN, which is the most poetic end to this course I could've imagined

Comments:
Whether you do this course or not, learning some proper statistics beyond what is taught in high school or 1st year maths is always really good knowledge to have. This course is inherently a more applications-focused one, but the first half of this course (probability theory) should appeal to you if youíre more into pure maths as I am. For a variety of reasons, this was the most difficult of the level 2 core maths courses so far for me. (Update: MATH2701, a much harder but also much better run course, now has this beat.) You really have to be on top of your game specifically when it comes to algebraic manipulations and calculus, as a lot of the side calculations that aren't even in the realm of probability or statistics anymore are sometimes very nontrivial. Thereís also a lack of any real intuition for the inference half of the course, so prepare for a lot of rote and having to take a bunch of things on faith.

What absolutely tanks the rating of this course is the teaching and organisation side, which was fairly disappointing this term compared to what Iíve heard about previous offerings. I truly could rant without end: the midterm, the assignment, the exam, basically anything. You name it, there was probably something wrong with it. A lot of what I have to say is just going to be straight shade though, so I wonít comment on specifics (I also donít want to rehash the points mentioned by a previous review from this term). That should give you enough of an indication as to my thoughts, and I can assure you that this isnít just a personal thing - the disappointment seems universal amongst those who did the course this term.

It borders on cliche at this point that so many "intro" statistics courses end up being of rather poor quality, and cases like this certainly do not help with that stereotype (at least as far as stats at UNSW is concerned). I already wasnít planning to do any further statistics courses after this, but I could certainly see how this would leave a sour taste in the mouths of those who are on the fence about their major and potentially turn them away, which is always a shame. If Donna is going to take this course again (Update: apparently she is in 2022), she definitely has much room for improvement, and I really hope she reads the constructive feedback she has been given this term and tries to take some of it on board.
« Last Edit: December 05, 2021, 06:23:33 pm by anomalous »

cherloire

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Re: UNSW Course Reviews
« Reply #273 on: September 08, 2021, 11:07:30 am »
+3
Subject Code/Name: COMP6771 - Advanced C++

Contact Hours:  2 x 2h Lectures (delivered over Youtube Live), 1 x 1h Tutorial (delivered over Zoom)

Assumed Knowledge: Formally, COMP2511 (only basic OOP concepts such as inheritance and polymorphism are drawn from this course)

Assessment:
- Assignment 1: STL containers/algorithms (15%)
- Assignment 2: Operator overloading, OOP (25%)
- Assignment 3: Templates and iterators (30%)
- Final exam: 3 hours online, 2 programming exercises (30%)

Lecture Recordings?  Yes, all lectures are archived on Hayden's youtube channel (you can watch all of them here, and also includes his other courses, which is great). Hayden's tutorial is also recorded each week.

Notes/Materials Available:  Slides to accompany the lectures are given, however they are sometimes pretty barebones, and often had errors or had not been updated since the previous offering, which is slightly annoying if you rather learn by reading notes rather than watching lectures, like me.

Textbook: Bjarne Stroustrup's textbook is listed as "If we had to point you to a single resource", but don't bother, the recordings/slides along with cppreference.com are more than enough.

Lecturer(s): Hayden Smith (with guest lecturers in week 10 from Optiver)

Year & Trimester of completion: 21T2

Difficulty: 2.5/5

Overall Rating:  4.5/5

Your Mark/Grade: 96 HD

Comments:
Despite the big self learning component of this course (which is basically wading through cppreference.com and S/O), this course has the best content out of any course I have learned so far. Hayden is a really great lecturer. One of the comments I've seen a few times about him is that "he doesn't seem very knowledgeable since he googles stuff in the lectures". I actually think this aspect of his C++ teaching is good - most of the time in this course you will spend navigating online C++ library specifications etc, and seeing him use these websites and picking up on the things he looks for when seeking an answer will become very relatable as students complete the assignments. The forum support (edstem) is excellent, shoutout to one of the tutors Nathaniel.

I really like the way the language is presented in the lectures, and students can immediately see the advantages of C++ over other languages they will have previously learned in CS at UNSW such as Java and C. One thing I wasn't aware of initially was that the course is more of a "C++ design course", in other words how to write C++ in a "correct" way (as there are many ways to do things in this language). The assignments are tailored towards this idea - rather than getting students to build cool applications in C++, the assignments are a means to reinforce good C++ practices. This did slightly get on my nerves a bit with the assignment marking though - different tutors marked assignments differently, and it seemed there wasn't much of a consistent marking criteria in some places, which became very obvious as I talked to other friends taking the course.

The assessment structure, being heavily assignment loaded and only a 30% exam, is a big plus IMO, and I think more CS courses should move to this model (if they haven't already). This is pretty rough however if you have a heavy workload term with other assignment loaded courses. The first question of the exam doesn't really test the learning done throughout the course, which was mainly from learning about and leveraging C++ features to complete the assignments. Instead it was an algorithmic type question that students could have completed before ever taking the course using C knowledge. The second question specification was a bit poor, many important details were left in a footer at the bottom, which took me a while to read as I was trying to decipher the overall question (thankfully these were only small parts in terms of marks). Despite this, I personally I thought the exam was reasonable, and could have done better if I was well slept and/or better prepared, however it was tight, and many students did not finish (or get close to finishing).
« Last Edit: September 08, 2021, 11:15:31 am by cherloire »

HelpICantThinkOfAName

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Re: UNSW Course Reviews
« Reply #274 on: November 18, 2021, 04:13:15 pm »
+2
Subject Code/Name: ECON2127 - Environmental Economics

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

Assumed Knowledge:  ECON1101. This will probably change next year to ECON2101, as the lecturer is considering making this a third-year elective. Even if it stays as it is, take 2101 and consider taking 2112. It'll make this course a breeze. Taking something like 3106 before as well made this course revision for the most part.

Assessment:

10% Tutorial Questions. They chose two or three problem sets we had to submit at the start of the tutorial. You can also just submit every problem weekly if you're not into that sort of thing.

20% Midterm. Nothing super difficult, just stay on top of the tutorial questions. Average was in the 70's.

2x10% Assignments. These were a little more difficult and had more parts that the tutorial problems. These just served as exercises to extend on previous tutorial problems and lecture material.

50% Final Exam. Questions similar in difficulty to the midterm, but focused on the latter half of the course.

Lecture Recordings? Full lecture and tutorial recordings available.

Notes/Materials Available:  Full slides and textbook chapters available.

Lecturer:

Dr Tess Stafford, 4.5/5. Tess sat in a two-hour consultation call every single week for whoever wanted to pop in and ask questions, which says enough about her - she's great to learn from.

Year & Trimester of completion: 2021/T3

Difficulty: 1/5.

Overall Rating:  5/5.

Your Mark/Grade: 85 HD

Comments: A nice and chill final econ elective to round out an otherwise hectic term. Would recommend to any econ student who is even mildly interested in the subject. I've also heard good things about its little sister ECON1107 (which I think some people doing environment degrees can use as an elective?), so consider that as well.
« Last Edit: December 16, 2021, 01:43:06 pm by HelpICantThinkOfAName »
Studying Economics/Mathematics @ UNSW

HelpICantThinkOfAName

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Re: UNSW Course Reviews
« Reply #275 on: November 22, 2021, 10:40:50 pm »
+2
Subject Code/Name: ECON3208 - Applied Econometric Models

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

Assumed Knowledge:  ECON2206 (or be enrolled in a Data Science degree and take MATH2831).

Assessment:

2x15% Assignments. We were given a dataset and a stata file, and a sheet of questions to answer. These questions were then tested in a multiple choice moodle quiz. A bit strange, but nothing difficult.

25% Group Project. This is an 8 page empirical research paper where we were given a dataset used in a paper, and then asked to answer the same question as the paper using the techniques described in lectures. They randomly assigned the groups within tutorials, or you could choose to do the project by yourself. 5% is from a team assessment, so if you've got a bad group you can flame them there.

45% Final Exam. 50 multiple choice questions in 2.5 hours.

Lecture Recordings? Full lecture and tutorial recordings available.

Notes/Materials Available:  Full slides provided.

Lecturers:

Mike Keane, 3/5. Mike taught the start and end of the course. His slides were a bit dense, but alright overall.

Fanghua Li, 4/5. Fanghua was pretty good for this course, can't really have asked much from her.

Year & Trimester of completion: 2021/T3

Difficulty: 4/5.

Overall Rating:  3/5.

Your Mark/Grade: 71 CR

Comments: A lot of statistics courses get (deservedly in my opinion) a bad rap for being deliberately obfuscatory and hard to follow. I can't really say the same about this course. Sure, it still has difficult content that takes time to wrap your head around, but it never felt like there was a need for the big conceptual leaps and blind acceptances of theorems that I felt were present in ECON2206. This course doesn't hold your hand, but it takes time exploring the big ideas before launching into a more in-depth exploration of the topics.

This course is essentially an extension to ECON2206, where you spend most of your time patching up the holes left behind by that course. Most of the lectures start with the premise of "here's something wrong with a particular regression, how can we fix it?", and then take a very logical path in ruling out what can and can't be done to fix that issue. This resulted in a course that felt a bit disjointed and lacking an identity of its own - you're constantly going back and forward between issue and solution, and not really considering if that solution would bring any issues as well.
« Last Edit: December 16, 2021, 01:43:20 pm by HelpICantThinkOfAName »
Studying Economics/Mathematics @ UNSW

fun_jirachi

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Re: UNSW Course Reviews
« Reply #276 on: November 25, 2021, 02:41:55 pm »
+5
Subject Code/Name: COMP2511 - Object-Oriented Design & Programming

Contact Hours:
2 x 2hr lectures
1 x 3hr tutlab

Assumed Knowledge:
Prerequisite: COMP1531 AND (COMP2521 OR COMP1927)

Assessment:
Assignment - 15%
Project - 35% (3 milestones over 4 weeks)
-- milestone 1 + 2 given two weeks, worth 17.5%
-- milestone 3 also given two weeks, worth 17.5%
Class Mark (Tutorials + Labs) - 10%
Final Exam - 40%

Lecture Recordings?
Yes

Notes/Materials Available:
Slides and tutor notes, lab exercises

Textbook:
Some suggestions for books that cover at least some of the topics in this course
Head First Design Patterns, by Elisabeth Freeman and Kathy Sierra, The State University of New Jersey
Refactoring: Improving the design of existing code, by Martin Fowler

Lecturer(s):
Ashesh Mahidadia

Year & Trimester of completion:
21T3

Difficulty:
2/5

Overall Rating:
-2/5 (adjusted from 0 pre-exam)

Your Mark/Grade:
81 DN

Comments:
The course was pretty okay for the first half - new raccoon (Refactoring Guru > Tom Nook), relatively tame assignment (albeit verbose and frustrating to work through, relatively easy to shrug off. I didn't like how long it was for the purpose it served; an intro to Object Oriented programming. It was easy but too long.), occasionally unnecessarily long lab tasks, etc. I could get past that, but then the course went to shit when the project happened. I'm going to also add that the labs that ran during the project were really long so learning stuff from the labs was lost on the students because of how bad the project ended up being. Retrospectively these only really served their purpose as study material for the exam, and as such the labs were often put out of context with the lectures at the time.

For context, we were told that the automarking process (which wasn't a thing in the previous offering of the course) was needed to ensure greater breadth in testing the correctness of students projects, which in turn awards fairer marks, particularly to those who completed more work. The only problem with these intentions (which I fully support, as they do make logical sense) was the execution was mindbogglingly poor, and the execution didn't achieve either of the objectives I've listed (correctness + fair marks) to varying extents, which will both be addressed below. There are also certain other reasons that I think are potentially partially responsible for the poor execution, but I won't go into those in depth because aren't as pertinent to the course itself as the following reasons. Just touching on them however I think is okay though -- it often felt like there could have been more hands-on support from course administration, especially when the course was in fact going awry but there wasn't for whatever reason (extra work, other commitments etc.). Nitpicking slightly, the announcements were sometimes inconsistent (ie. we won't give you X input / we won't test you on Y case, then those events actually happened, stuff like that).

But anyway, the main spiel:
From the start, the timeline that seemed to be employed should've rung alarm bells. Two weeks per milestone is not bad, though more time is preferable. But when the assignment is split like it is, and the second "half" of the assignment depends hugely on the first "half" (the whole point of the second bit is how well your design in the first adapts to new criteria. To quote the project specification: "60% of your [Milestone 3] automark will come from testing a completed interface which includes all the requirements in Milestone 2, and incorporation of the following new requirements (1.1 to 1.4).") it's imperative that students get feedback really quickly. There are two weeks between the two due dates, and as such two lab sessions. However, due to the structure of the course, we demonstrate our product to our tutors in the lab session immediately following the first due date and receive feedback in the next. Depending on when your session is (or if your tutor decides to give feedback outside lab time), the time remaining to act on that feedback for the final product may vary from anywhere between 4-7 days. This is particularly nitpicky but it certainly isn't the worst part, because that title is reserved for the various shenanigans that automarking created. I have no other words to describe automarking other than genuine shit because a) as stated before the execution was awful, b) the process to remedy this was equally if not more awful and c) the automarks (which genuinely could have been released earlier, unless for an even weirder reason the autotesting suite wasn't available before the automarks were released (this would point to admin unpreparedness)) were released really damn late ie. they were released 5 days from the milestone 3 deadline. This course already has an implicitly high workload attached to it, but these late results made us scramble harder (and unnecessarily so, IMHO, since it was in no way our fault), especially since not many of the errors the autotests raised for groups were particularly helpful in pointing out actual flaws in groups' programs. It was genuinely enraging at the time, and even in hindsight, and remaining somewhat level-headed it's impossible to describe it as anything other than a complete shocker. The flow-on effect of this late release and failure to accomplish the initial rationale set for automarking was that despite it being no fault of the students, students had close to no time to fix these non-errors in milestone 2 because of the looming milestone 3 due date. It became a dilemma between working on milestone 3, which relied on the "buggy" milestone 2, or maximising the previous marks and sacrificing milestone 3. For context, you would have been likely to fail other autotests in milestone 3 similar to those in milestone 2. In the end many groups had no choice but to go with the latter option because of the hanging threat.

Now, addressing the remarking process (ie. "b) the process to remedy this was equally if not more awful") -- the initial remark was slated to be returned on the Saturday before the Monday due date, IIRC, which to a student is absolutely outrageous. The amount of organisational disarray would have been ridiculous. We had no dry runs prior to the submission for Milestones 1 + 2 ie. nothing, even the most basic stuff just to ensure we wouldn't fail on technicality rather than incorrectness. This would have prevented a lot of the problems that arose. The official? reason for not providing a dry run was that it'd give away the testing suite, which seemed weird and remains so. A LOT of groups failed on dumb technicalities, and even a remark wouldn't have solved this because there were so many technicalities that a single remark may have solved one only for your group to uncover another. Despite this literally being in no way the students' fault, it was made out to be as if it was. We weren't allowed to "debug" -- but many groups just wanted to fix the technical errors as opposed to logic errors, ie. the ones that the autotests wouldn't facilitate, which weren't even wrong in the first place. In the end, dry runs were released for milestone 3 (any away from the actual testing suite would have been okay for milestone 2) but these ended up being provided two days after the automarks were actually released and were lacklustre at best. They were just the most basic reused milestone 2 tests.

Other issues related to remarking include but aren't limited to:
- The use of a marking cap to allow for small incremental errors/differences between the tests and groups' work, however, this initiative failed for multiple reasons; as stated elsewhere, because of how the autotests ended up running, one reason this failed is that this came off as an implication of a poor specification, rather than assumption variation. The autotests were capped at 80-90 which wasn't particularly helpful at first since a lot of groups initially got way lower than that. I will concede something below
- There was a remarking penalty for "non-atomic changes" which were often necessary for some groups because the set of changes classed as atomic was (somewhat) objectively narrow. This penalty was kept in place even after the shitshow this ended up being, which I personally thought was rather ridiculous (it wasn't even reduced, but I'd like to think it was adjusted slightly behind the scenes, despite the max 20% penalty still being a thing)

I will concede though, that this whole process would have been acceptable had the autotests worked as intended (with a provided dry run, of course) but as it didn't, it just made everything a whole lot worse. Another concession; you did get the highest mark of all the remarks, but this I think pales in comparison to how bad automarking ended up being.

The last point (ie. "a) as stated before the execution was awful"); the biggest problem here was that a lot of the project was open to interpretation, which a lot of the autotests did not factor in. While there was good breadth in testing, what they ended up doing was going into too much depth, thus by definition making assumptions which in many cases conflicted with the more than valid assumptions made by some students. We were told that we should make assumptions and were encouraged to do so where necessary, then we essentially got screwed for doing the exact thing we were told to do ie. basic errors not cleared up by the specification and were fair assumptions ie. no questions required on the forum were causing autotests to screw up, but we didn't know what these "errors" were. We were also told that the autotests would test "lower level / general stuff" and NO edge cases but this was in general not true (some tests fell under the general umbrella of "edge case", others tested higher level stuff where by definition students' interpretation comes into play). A phrase that I saw another student use that encapsulates this whole saga rather well is that "you're allowed to make assumptions, as long as they're also the ones we make", which is frankly ridiculous. If the specification and autotests needed X assumption to pass autotests, these should have been explicitly stated in every case, not just a select few (which I will give *some* credit for) and vaguely elsewhere. I also saw a student say something along the lines of "the project uses design by contract but essentially expects us to defensively program". It's just a shame because overall, autotesting is worth 14% of your OVERALL grade ie. for some rather extreme context, getting 0 for automarking in total can drop you from 100 almost down to a Distinction. It's even more of a shocker when the autotests didn't do their job properly, and even more so when you realise that autotesting was worth more than design in what is fundamentally a software design course (1.33x more, if I recall correctly).

An example of a really bad test that was actually given:
For context, we made a dungeon crawler game. A particular enemy can spawn and has a chance of spawning with armour. That chance is arbitrarily decided by your group. However, there was a test in the automarking suite you could fail if NONE of the first ten of that enemy spawned with armour ie. if you assumed this enemy had a 10% chance of spawning with armour, you'd fail this test roughly 1/3 of the time. This test was purely luck-based, and just statistically favours those who arbitrarily chose a higher chance of armour spawn. Now, this particular test wasn't worth a lot (given the number of tests in the testing suite), but when this sort of thing crops up multiple times across the testing suite, you can imagine the fury of the students. How this particular test was a good idea, I'll never know.

Other pertinent points:
- The response to criticism was passive and slow. Some feedback ran along the lines of "go read the spec", "don't worry about it", etc. There was also a 15m ish window where the course forum had temporarily disabled public posting/commenting, which seemed really strange given the timing (at the peak of the complaints and student anger). Even considering how long it took to get marks, it felt like it took longer to took forever to get responses and feedback on criticism of the automarking process. In short, lack of transparency, stability and communication
- I personally found it weird that no deadline extension was ever on the table (even though many students had made it clear that an extension wouldn't fix things in private circles). The only one afforded to us was the 5hr one for a 5hr GitLab outage in the first submission. I can guarantee that this ended up slowing students for a lot more than 5 hours, even though a deadline extension would have just extended the pain
- Groups with bigger issues that couldn't be resolved by a remarked automark received manual marking, but on a large scale, this was unfeasible. It felt really selective, and I can imagine that a) some groups may not have been bothered anymore and b) many had bigger issues. It would have been better to have executed this properly the first time given the problems that have existed in this course from previous offerings. Having success after manual marking just felt bittersweet; it felt really damn wrong to have to blunder through all this bureaucratic BS just to get correctly assessed.
- If code coverage was high enough, it's worth wondering if using each group's testing suite may have actually been fine, but that's a point for another time.

It's a shame because this course genuinely has potential; OOP as a concept is pretty interesting, but like many other courses (especially certain ones I've taken previously), off the mark administration ruins the student experience. I took two courses and was still occupied ie. a disproportionate workload. It's hard to believe I was considering taking another course at the start of term, and I couldn't be happier that I didn't after how this turned out. I should also reiterate that this is NOT in any way an attack on the course staff; they clearly had the right intentions and the right rationale for their changes. It just so happens that the final product was a devastatingly poor student experience. I might add; the project is worth 35% of your total grade, the labs are a portion of 10% but I have in fact taken more away from the labs given how panic-inducing this project has been; I've also never seen an effort vs marks ratio this disproportionate, even in some parts of HSC English.

Post-exam: Literally all the problems pre-exam were compounded. I went into the exam a bit more open-minded and hoping for improvement, which unfortunately never came. The exam itself was shocking. I would not be surprised if many people failed the 40% hurdle (raw marks, before any scaling).

I will give them the fact that the theory part of the exam was pretty smooth sailing, and well written. The programming questions just about summed up the whole term. The questions were too long, too hard and too verbose. Difficulty wise: literally none of the stuff we were told to prepare with (sample questions, lab questions, tutorial questions) could match up to this in the programming section. The prep was piss-easy, this was notoriously difficult. The prep absolutely paled, and the samples were largely irrelevant because we'd seen the questions as lab problems as well. In any case, I would imagine some if not most of the students who did the recommended preparation would have been 100% screwed, which speaks to the ridiculousness of the exam.

You basically had two choices: plan out your response or dive straight in. Either way, you'd encounter time drains; diving straight in meant you couldn't properly tackle the problem, which would have been evident for a course literally called Object-Oriented Design and Programming. Planning out your response would have taken too long (as it did for me, after which I panicked and ended up half-arsing a plan and a response), leaving you with not enough time to complete the exam. The sheer verbosity and length of the exam meant it was impossible to finish; I doubt the writers of the exam took it, nor even gave it to a tutor to try because this was just frankly ridiculous. Given six hours, twice the allocated time wouldn't have saved the majority of the cohort (and it would have extended the pain and confusion anyway), who were post-exam making jokes about "haha see you next year guys". If last term's exam was just "bad" (or so I have heard), I have no choice but to brand this one absolutely fucked. I have never taken an exam written worse, nor had an exam experience worse than this, EVER (regardless of if it was self-sabotage, as has happened before, or the fault of the people involved in running the exam). It's telling that I've enjoyed courses while not doing so well and will merit courses regardless of my mark, so I think for this offering of the course I'm being more than fair.

Again, this course absolutely has the potential to be a good course, but this offering has been nothing short of shocking. I really thought the automarking saga was rock bottom, but as it turns out there was an even rockier bottom underneath. I wanted to rant more, but I'm honestly so done with this particular offering of the course; I think the fact that a) I've bumped my course rating down to NEGATIVE two says enough, and b) "I have never taken an exam written worse, nor had an exam experience worse than this, EVER" says more than enough about a course already rated 0.
« Last Edit: December 17, 2021, 11:04:34 am by fun_jirachi »
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HSC 2018: Mod Hist [88] | 2U Maths [98]
HSC 2019: Physics [92] | Chemistry [93] | English Adv [87] | 3U Maths [98] | 4U Maths [97]
ATAR: 99.05

UCAT: 3310 - VR [740] | DM [890] | QR [880] | AR [800]
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HelpICantThinkOfAName

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Re: UNSW Course Reviews
« Reply #277 on: November 29, 2021, 07:36:18 pm »
+2
Subject Code/Name: ECON3123 - Organisational Economics

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

Assumed Knowledge: ECON2101 or ECON2112. I'd recommend taking both before this course.

Assessment:

4x10% Problem Sets. Two or three problems that are a bit more difficult than what was shown in tutorials or lectures.

60% Final Exam. Similar structure to the problem sets. Three questions with multiple parts. Some with calculations, and some asking you to verbally explain the underlying contract structure.

Lecture Recordings? Full lecture recordings on hand.

Notes/Materials Available: Full slides provided.

Lecturers:

Hongyi Li, 3.5/5. This might not be a fair score for Hongyi since I had Gabriele, Federico, and Gautam last term for other third-year micro courses - I'm a bit spoiled! I've had friends say they he was one of their favourite lecturers. I enjoyed his lectures, and his notes were very comprehensive.

Year & Trimester of completion: 2021/T3

Difficulty: 4/5.

Overall Rating:  3/5.

Your Mark/Grade: 75 DN

Comments: This is course should really be called Contract Theory. We spent all of our time investigating interactions between principals and agents (essentially just employers and employees) under different circumstances. Principals will have one set of desired outcomes (maximise profits), and agents have another, often conflicting, set of desired outcomes (maximise pay). The fun of this course comes in playing around the different times that the principals and agents make moves, how the principal pays the agent, and how the agent produces the good. I found the weeks spent on Asset Ownership and Career Incentives to be particularly interesting because of how fun it was to keep track of all the different variables and timings that were introduced.

There are a couple of weeks in the middle that I thought were a bit of a slog - the lectures on Performance Evaluation, Teamwork, Incentives, and Authority. They each took me a while to understand the underlying interaction, but I can't say that I'm very comfortable with them.

Overall, this is a pretty fun course. I wouldn't recommend that you take this over courses like ECON3106 or ECON3121 though.

Aaaand that's it for my undergrad degree! It's been a great ride for the last four years at UNSW, even with the chaos of 2020 and 2021. I hope that my course reviews have been comprehensible and useful for everyone who has read them. I might be doing econ honours next year, so keep an eye out for a review on that at the end of next year if I'm not burnt out at the end. Thanks everyone!
« Last Edit: December 16, 2021, 01:43:42 pm by HelpICantThinkOfAName »
Studying Economics/Mathematics @ UNSW

fun_jirachi

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Re: UNSW Course Reviews
« Reply #278 on: December 10, 2021, 09:36:17 pm »
+2
Subject Code/Name: MATH2621 - Higher Complex Analysis

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

Assumed Knowledge:
Prerequisite: MATH1231 or DPST1014 or MATH1241 or MATH1251 each with a mark of at least 70; Exclusion: MATH2069

Assessment:
2 x 20% Class Tests
60% Final Exam

Lecture Recordings?
Yes

Notes/Materials Available:
Yes

Textbook:
None

Lecturer(s):
Dr Arnaud Brothier

Year & Trimester of completion:
21T3

Difficulty:
2.5/5

Overall Rating:
4.5/5

Your Mark/Grade:
91 HD

Comments:
I can only really criticise this course for its annoying timetabling, and that's being *really* nitpicky.

Ridiculously good course. The exams were fair and well structured (both the exams themselves and the assessment structure overall [see assessments]), the teaching couldn't be faulted and the content was brilliant. When you talk about maths clicking in a satisfying manner, this is definitely it. To find topics that get tied up as elegantly as the ones covered in this course has been somewhat rare so far, and has been much appreciated this term. Up there with one of the best courses I've taken, full stop; pick anything in the course and you could probably find at least three things great about it. Not much else to say, except just take this course if willing and able.
« Last Edit: December 17, 2021, 11:04:10 am by fun_jirachi »
Spoiler
HSC 2018: Mod Hist [88] | 2U Maths [98]
HSC 2019: Physics [92] | Chemistry [93] | English Adv [87] | 3U Maths [98] | 4U Maths [97]
ATAR: 99.05

UCAT: 3310 - VR [740] | DM [890] | QR [880] | AR [800]
Guide Links:
Subject Acceleration (2018)
UCAT Question Compilation/FAQ (2020)
Asking good questions

Opengangs

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Re: UNSW Course Reviews
« Reply #279 on: December 13, 2021, 11:48:54 am »
+2
Subject Code/Name: COMP4418 - Knowledge Representation and Reasoning

Contact Hours:
- Pre-recorded lectures.
- 2 x 2 hour live interaction sessions that act as consultations.

Assumed Knowledge:
The official pre-requisite is COMP3411, although you don't need any knowledge from that course at all. Some proof logic would be useful for the logic topic.
 
Assessment:
- 3 x assessments (45%; 15% each)
- Final exam (55%)

Lecture Recordings? Yes.

Notes/Materials Available: Lecture slides are sufficient.

Textbook:
None prescribed.

Lecturer(s):
- Dr. Maurice Pagnucco (Formal Logic and Reasoning)
- A/Prof. Haris Aziz (Reasoning about action, multi-agent resource allocation, social choice, cooperative game theory)
- Dr. Abdallah Saffidine (Answer-Set Programming and Clingo)

Year & Trimester of completion: 2021 Term 3

Difficulty: 3/5

Overall Rating: 3/5

Your Mark/Grade: 77 DN.

Comments:
This course felt very... meh. It didn't feel like I learned anything particularly useful if I want to go into Artificial Intelligence, and very much just felt like three separate courses squeezed into one. The course is split into three parts. The first two weeks delves into the different logic systems, including propositional logic, first order logic, and Horn logic. You learn how to prove statements, both semantically (interpreting its meaning) and syntactically (by manipulating symbols to arrive at other statements). The advantages and disadvantages of each logic system is explored and gives further motivation to why we should care about other logic systems (for example, our logic system should be complete and sound, yet simple and easy to adapt to new rules). I found this to be the easiest of the three topics because it's the topic that is the most familiar to me.

The next few weeks were exploring how agents share resources and how they should distribute bundles among each other so that certain properties of efficiency and fairness are respected. For example, if certain people express dissatisfaction about a candidate, their voice should at least be considered (which is something that's further explored in social choice). I found this topic to be... the most disconnected(?) of the three. It seems like this is a very specific field of artificial intelligence that should be taught as an economics course, not in this course. Even though the course content was interesting enough for me to continue, I often asked myself what the entire point of this topic was and found it hard to relate to aspects of artificial intelligence. The lecturer for this topic wasn't the best either; he didn't really go over how to do computations, but rather left it as an exercise for the viewer. So it took me longer to figure out what was happening than it needed to be. Even in the live session, he glossed over the computations and decided to focus on the less computationally heavy aspects, only for the bulk of the computations to appear in the final exam.

The last few weeks were left to explore a rather esoteric language. The notion of Answer-Set Programming is to convert a problem statement into a computational model whose answer sets correspond to solutions to the original problem statement. The theory of the last topic was interesting enough since this is a rather new way (not really new, the language is more or less the same as Prolog) to view problem solving. The interactive session was dedicated to solving NP-hard problems, Abdallah did a great job at motivating the use of answer set programming and teaching us the tricks of the language.

Overall, I felt that the course could have improved by making the link between the three topics a bit clearer and by motivating the middle topic a bit more. On their own, the topics would be interesting for anyone who just wants to learn new things. But if you're serious about artificial intelligence, it just felt very clunky and disjoint.
« Last Edit: December 16, 2021, 12:39:53 pm by Opengangs »

Opengangs

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Re: UNSW Course Reviews
« Reply #280 on: December 13, 2021, 12:55:40 pm »
+3
Subject Code/Name: COMP4920 - Professional Issues and Ethics in Information Technology

Contact Hours:
- 2 hour lecture
- 2 hour tutorial

Assumed Knowledge:
You're expected to be in the last year (or close to) of your computing degree. I strongly urge you to find a group of around 3-4 people you're comfortable working with before enrolling into the course because it's going to be a pain if you don't.

Assessment:
- Seminar participation (10%)
- Lecture summaries (10%)
- Movie Review (20%)
- Student seminar (20%)
- Essay (40%)

Lecture Recordings? Yes.

Notes/Materials Available: Lecture slides are sufficient.

Textbook:
The prescribed textbook is Ethics for the Information Age but the course admin has said that it's a bad textbook for ethical argumentation soooo....

Lecturer(s):
- Course admin: Dr. Wayne Wobcke
- Lecturers: Dr. Stephen Cohen (ethical theories) and David Vaile (law)

Year & Trimester of completion: 2021 Term 3

Difficulty: 2/5, but this is difficult to judge because the marking seems somewhat questionable.

Overall Rating: 0.5/5

Your Mark/Grade: 73 CR.

Comments:
Oh man, where should I start? I'll probably start by praising the course for their efforts in improving the course from 2020's offering. Reducing the seminar participation from 20% down to 10% is a big win for the 2021 cohort because, otherwise, it's a battle royale of people just shouting answers as loud as they could. The lecturers (Stephen and David) couldn't be faulted, they were engaging enough to keep me going. That is all, onto the criticisms now.

The enjoyment of the course very much depended on whether you had a decent tutorial or not, whether your tutor enforced turning on your camera (thankfully my tutor was very laidback about it), and whether you could deal with students speaking for 5 minutes about absolutely nothing at all. About 50% of the course depended on the tutorial: 10% on the seminar participation, 20% on the movie review where you find your partner in your tutorial, and 20% on the student seminar where you find a group of 4-5 within your tutorial. So yeah, if you don't have a decent tutorial, you'll have a hard time with the course, which is why you should ideally do it with a group of friends so that the pain of enduring the course is shared among the group.

The course assessment structure was confusing. Lecture summary requirements were rather ambiguous. Wayne mentioned that the lecture summaries could not exceed one page per summary without any other description of page margins, font sizes (okay he said 9-11pt, but different fonts have different heights which drastically change the number of words), and font faces. When this was rectified, there was no major announcement and we, as students, are expected to find the updated change. This seems almost intentionally malicious but I'll believe that Wayne just forgot to make an announcement that a word limit was set. The movie review was pointless, the feedback was meaningless because it was vague. It was introduced just so that students didn't use The Social Dilemma as a reference for their essay. However, the structure of the movie review was just confusing. It was a mix of a self reflection and a review of the documentary in which the feedback wouldn't be relevant for the actual essay, despite Wayne saying so.

The marking seems a bit harsh and somewhat arbitrary. It seems like not many groups can achieve 17+/20 for the student seminar unless they do something extremely creative and out of the park. The feedback didn't really match the criteria and they were vague and unhelpful. Comments like "deeper ethical discussions" do not help unless you can pinpoint which areas required deeper ethical discussions and what constitutes as "deeper ethical discussion". One seminar tutorial should be dedicated to ethical argumentation and ways to strengthen those reasonings instead of looking at different scenarios (believe me, that's a good thing; we just have too many of those laying around right now). It seems like the tutorials are developed so that the tutors don't have do much since their comments tend to be short and unhelpful with the occasion "good point, [insert longer quote]".

Overall, I'm led to believe that Wayne has good intentions but I feel that he's a bit lazy which makes it seem like he's intentionally trying to fail people despite the fail rate being close to non-existent. With a change in administration, this course could have the potential to be really good. Ethical reasoning is always an interesting discussion but that can only be done when the administration is fixed. Tutorials should be tailored towards developing and strengthening ethical discussions and reasoning. Assessment feedback could be a bit deeper so that it's more helpful for future assessments.
« Last Edit: December 16, 2021, 12:40:28 pm by Opengangs »

Opengangs

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Re: UNSW Course Reviews
« Reply #281 on: December 16, 2021, 12:56:39 pm »
+2
Subject Code/Name: MATH3871 - Bayesian Inference and Computation (postgraduate equivalent)

Contact Hours:
- 2 hour lecture
- 1 hour tutorial
- 1 hour lab

Assumed Knowledge:
The official prerequisite is MATH2801 or MATH2901. The content on law of large numbers will be pertinent for the discussion of the theory of Monte Carlo methods.

Assessment:
- 2 x class tests (35%; 15% and 20%).
- 5 x quizzes (5%; 1% each).
- Final exam (60%)

Lecture Recordings? Yes.

Notes/Materials Available: Lecture slides are sufficient.

Textbook:
None prescribed.

Lecturer(s): Dr. Clara Grazian

Year & Trimester of completion: 2021 Term 3

Difficulty: 3.5/5

Overall Rating: 4/5

Your Mark/Grade: 92 HD.

Comments:
A fantastic and highly practical course that shies away from classical statistics. The course is split into two major themes: the theory of Bayesian inference, and the practicality of implementing Monte Carlo methods which is arguably the most important aspect of the course. There is equal weighting between the theory and the practical component so you should familiarise yourself with both aspects of the course. And in fact, the way to understand the theory is to understand what you're implementing when you're in the labs. This was how I understood the Monte Carlo methods. The assessments were relevant to the lecture content and the lecture slides were more than sufficient to do well in the course. However, because the tutorial and the lecture slots were so packed together (lecture was 7-9PM on a Tuesday evening and the tutorial was 9AM on a Wednesday morning), I often found myself not having any time to complete the tutorial problems until the first week of stuvac. In short, I basically learned how to do each of the problems without attempting it first during the tutorial.

In saying that, if you need an elective and don't mind the challenge to learn something interesting, I would recommend this course. Just don't neglect either the coding or the theory, and you'll be fine.

RuiAce

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Re: UNSW Course Reviews
« Reply #282 on: December 16, 2021, 10:38:35 pm »
+4
I wholeheartedly despise this course

Subject Code/name: COMP4920 - Professional Issues and Ethics in Information Technology - Course renamed in 2021

Contact Hours: 2hr lecture in weeks 1, 2, 3, 5, 7 (note: week 4 was a public holiday), 2hr seminar (tutorial)

Assumed Knowledge: Not really anything to do course content. Prerequisite is COMP2511 (or the old COMP2911), and have completed 96 UoC within a computer science degree, but really this just reflects that you should be towards the end of a CSE degree and hence have the background of a software engineer.

Assessment:
- 10% seminar participation
- 10% lecture summaries
- 20% movie review (reflection)
- 20% student seminar
- 40% company case study

Lecture Recordings? Yes

Notes/Materials Available: Mostly just the lecture slides. However, some seminars also required preparation material, which was provided.

Textbook: Quinn, M.J. Ethics for the Information Age. Eighth Edition. Nobody used it though to my knowledge.

Lecturer(s): A/Prof Wayne Wobcke. He only really delivered the first lecture though. Content was instead delegated to guest lecturers: A/Prof Stephen Cohen, David Vaile, and members of FPA Patent Attorneys Pty Ltd.

Year & Trimester of completion: 21T3

Difficulty: 3/5

Overall Rating: -2/5

Your Mark/Grade: 76 DN

Comments:
This is a core course to all CSE students that don't have SENG4920 required instead. Note that in 2021, SENG4920 was an identical course to COMP4920, and students between COMP4920 and SENG4920 could partner together for the movie reflection and the student seminars. (Effectively, the "management" component of the course has now been completely phased now, which justifies the course name change.)

Clearly, I hated every moment of this course. But when comparing to the aforementioned review, one should at least observe that -2 is a better rating than any number from -5 to -500. This is because improvements should be acknowledged where due. Some of the more noticeable improvements from last year:
- Essay plans were actually reviewed
- Removal of the second student seminar from 2020 certainly freed up more time
- Participation cut from 20% to 10%, which made it feel considerably less like a warzone trying to participate in the seminar.
- A couple admin improvements as well. (For example consultation hours, though I didn't use these specifically myself.)

Also, in all fairness the content is fine. And the best thing about the course for me was the absence of a final exam. This freed up my time a lot at the end, and this I am thankful of. (Oh and also, I found out my final mark before release of results day because of this.)

So why is the rating still non-positive? Because:
- Although the documentary itself wasn't bad, the movie review task itself was nothing but confusion. Many marks were lost for things that were just bizarre. 750 words also feels way too short for ethical argumentation and reflective components to be mixed together.
- Feedback received was often ambiguous. It felt like a plus that marking guidelines were given, but sometimes the feedback received just seemed to have nothing to do with it. The feedback was also usually very brief and hence unhelpful.
- For some reasons HDs are just a non-existent concept in this course. Though DN is very much possible (including DNs higher than the mark I received), HD seems to be eliminated altogether.
- Hard limit for the essay was certainly a benefit. But alongside everything else we needed to include, it just seemed impossible to have stuff like "stakeholder perspectives" covered with the 1500 words we were given.
- It's never clear what the right balance of stuff is. How deep should the ethical arguments be? How do we incorporate everything (e.g. background context, the "stakeholder perspectives" mentioned above)?
- Information surrounding the lecture summaries was often unclear, or ambiguous.
- Oh, and not being taught how to write. Movie reflection and essay both suffered here. (Perhaps also student seminar, but can't really comment here.)
- (On a personal note, I find it hard to envision when I'd ever use Kantian ethics to analyse behaviour in the workplace.)

The level of subjectiveness in the marking really makes me question what arts faculty students have to go through when they take courses similar to this. It's hard to not believe that deliberate limitations are placed on each student denying them from truly feeling any "success" in the course. (Some people also argue that some of the marks released are really just an RNG.)

Often times, it was very unclear to me why this course felt necessary. I do support the concept of an "ethics" course in the 21st century. (And again, to be fair the content itself is not bad.) But expecting me to be satisfied with something that hinders any sense of academic accomplishment is a completely different matter. No wonder some students seemed to run off into the electrical engineering version (despite that one having a final exam and blah).
« Last Edit: December 22, 2021, 11:22:23 am by RuiAce »

Opengangs

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Re: UNSW Course Reviews
« Reply #283 on: December 20, 2021, 10:30:25 am »
+4
Subject Code/Name: MATH5645 - Algebraic Number Theory

Contact Hours:
- 1 x 2 hour lecture and 1 x 1 hour lecture
- 1 hour tutorial

Assumed Knowledge:
There are no formal prerequisites for the course since this course is expected to be taken in your Honours year. But if you plan on taking it pre-Honours (like I did), then you are expected to achieve an average mark of 70 in MATH2601 and one of MATH3711/MATH3521/MATH5706. It is highly recommended that you take MATH3711/5706 because the content from that course will be fruitful for the discussions in this course.

Assessment:
- Weekly problem sets (10%; top 4/10 will be counted towards the 10%).
- Assignment/project (30%)
- Final exam (60%)

Lecture Recordings? Yes.

Notes/Materials Available: Lecture slides are sufficient.

Textbook:
None prescribed.
Recommended: Number Fields by Daniel Marcus.
The course seems to follow closely to this book.

Lecturer(s): Dr. Alina Ostafe

Year & Trimester of completion: 2021 Term 3

Difficulty: 4/5

Overall Rating: 4.5/5

Your Mark/Grade: 83 DN.

Comments:
One of my favourite math courses thus far, and really consolidated why Iím choosing to do a Number Theory / Combinatorics thesis when I start my honours in 2023. It is essentially a follow on course from MATH3711; you begin with some brief discussions on field extensions because everything you do from that point forward assumes you know what a field extension is. There are a lot of MATH3711 content so if you plan on taking this before/during honours, ensure that you review MATH3711.

The assessments were split into three different sections. You were assigned weekly problem sets and a random question was picked out of 6-7 problems to submit for marks. This helped me stay up to date with the lecture material. At around week 8, you had to complete a mini project in the style of a small research paper which contributed 30% of the final grade. Finally, a 3 hour final exam which consisted of 6 questions. Overall, the assessments were fair and marking was lenient with really good feedback. 10/10 recommend.

Opengangs

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Re: UNSW Course Reviews
« Reply #284 on: April 26, 2022, 09:14:07 am »
+4
Subject Code/Name: MATH5515 - Special Topics in Pure Mathematics A (Introduction to the Riemann Zeta Function)

Contact Hours:
- 1 x 2 hour lecture and 1 x 1 hour lecture
- 1 hour tutorial

Assumed Knowledge:
There are no formal prerequisites for the course since this course is expected to be taken in your Honours year. But if you plan on taking it pre-Honours (like I did), then you are expected to have completed MATH2621 and it is recommended to have taken some abstract algebra and analysis since we also deal with group homomorphisms when talking about Dirichlet characters.

Assessment:
- Assignments (45%; 15% each)
- Final exam (55%)

Lecture Recordings? Yes.

Notes/Materials Available: Full lecture notes are available.

Textbook:
None prescribed.

Lecturer(s): Dr. Lee Zhao

Year & Trimester of completion: 2022 Term 1

Difficulty: 4.5/5

Overall Rating: 4/5

Your Mark/Grade: 84 DN.

Comments: Quite an interesting course but quite difficult as expected. The course extends MATH2621 with a deeper focus on the number theoretic topics of analysis. With a primary focus on the Riemann Zeta Function, the course serves as an introductory class on many areas within analytic number theory which is refreshing to see.  Dr. Lee Zhao does an excellent job at not only teaching the topics but offer historical insights into the development of such topics. His exposition, both orally and written, is interesting to read and listen to.

The assessments are what you expect in a Level 5 course. They're not overly difficult but they require you to develop intuition and insights about the results examined in class. I wholeheartedly recommend this course if you enjoyed Complex Analysis and it really does the field of analytic number theory justice.
« Last Edit: May 19, 2022, 05:07:14 pm by Opengangs »