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UNSW Course Reviews

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Justin_L:
Subject Code/Name:
ENGG1000 - Engineering Design and Innovation
Project eEVee (Evolving Electric Vehicles for Emerging Economies)

Contact Hours: Depends on project

Assumed Knowledge: None

Assessment:
NOTE: Varies based on project
Impromptu Design Writing Task - 5%
Design Journal (checked twice) - 15%
Engineering Design Process - 15%
Professional Communities - 10%
Team Evaluation - +/-25% (Used to moderate marks within teams)
Design Performance - 20%
Design Commercialisation - 15%
Final Report- 20%

Lecture Recordings? Yes, available on Moodle and Teams

Notes/Materials Available: Depends on what project you chose

Textbook: Dym, C.L. and Little, P. (2014). Engineering Design: A Project-Based Introduction, 4th edition, John Wiley and Sons (Not required)

Lecturer(s): Varies based on project

Year & Trimester of completion: 21T1

Difficulty: 1/5

Overall Rating: 2/5

Comments:
For context, ENGG1000 is an introduction to design course generally taken in the first term of any engineering degree, and is setup so that students can preference projects to choose (I've attached the list available in T1, feel free to PM me if you want the detailed project pitch for anything)

21T1 ProjectsAirborne Terrain Mapping
Autonomous Container Delivery
Battling the Big Dry
Bionic Hand
Project eEVee
Impact-proof Buildings
Mars Regolith
National Emergency Supply Equipment
Renewable Energy from Waves
Robots to the Rescue
SunRay Speedway
In response to previous reviews, the Impromptu Design Challenge has been moved to the start of the course. While this is nice in terms of not being disruptive, it also meant that we only got our group and project assignments well into in Week 3, which is a pretty significant amount of time into the term for a Trimester considering that we also had to do safety and lab inductions before we could even start work.

I picked and got into Project eEVee (Evolving Electric Vehicles for Emerging Economies), a chemical engineering project focused around designing an effective battery to drive a lego car, with the actual electric vehicle component being secondary. This project requires that you supply your own PPE (Lab Coat, Safety Goggles, and Face Mask) as well as some other precautions like closed shoes and long pants to be able to work in the chemical engineering labs.

While this project specifies no prior knowledge is necessary, you're basically screwed if you haven't taken HSC Chemistry or equivalent. While the course offers technical lectures on electrochemistry, it's simply not enough to facilitate more advanced cell designs which require a strong foundation in chemistry. Similarly, little to no support is given for the car design and things like torque, current, and gear ratios are never taught. I felt that I learnt more from my group members than from the teaching staff, which is a problem when you consider the massive disparities in skillset between groups.

Overall, I think this would be a fun course if you were adequately prepared and had the background knowledge to work systematically and effectively. As it is now, it feels like you're being thrown headfirst into a project with little to no support, with your success being entirely determined by the team you're assigned. While I was lucky to have a good team who was able to teach me a broad range of concepts, I know friends who ended up learning little to nothing in their projects as well teams who couldn't get a working battery by the end and so couldn't participate in final testing. With only 6 effective weeks to work on the project, it was difficult to do anything meaningful and to develop the technical knowledge needed before actually starting work.

I also dislike the assessment format, with things like structured reflections feeling very forced in that questions are specifically written to direct you towards certain insights to get marks. The Professional Communities assignment was also a bit strange in that it gave credit to people who attended camps and required you to create a LinkedIn profile. In the end, you do get exposure to lots of different areas of engineering (at least in this project) but I agree with earlier reviews that the course seems like a massive waste of time, and that these skills could be much more effectively gained through things like participation in student led projects and internships.

fun_jirachi:
Subject Code/Name: MATH2111 - Higher Several Variable Calculus

Contact Hours:
2 x 2 hour lectures, 1 x 1 hour lecture
1 x 1 hour tutorial (there were three separate tutorial sessions, you were free to attend anywhere from 0-3 sessions)

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

Assessment:
10% Week 4 MapleTA Quiz (repeatable quiz) + Hand-in Proof
20% Week 7 MapleTA Quiz (repeatable quiz) + Hand-in Proof
20% Week 10 Class Test
50% Final

Lecture Recordings?
Yes

Notes/Materials Available:
Yes, supplied on Moodle

Textbook:
None that I can recall being mentioned

Lecturer(s):
Anita Liebenau, Guoyin Li

Year & Trimester of completion:
21T1

Difficulty:
3/5

Overall Rating:
5/5

Your Mark/Grade:
78 DN

Comments:
In short, brilliant course. While some concepts were difficult to wrap my head around at first, every single thing taught was taught extremely well, and the assessment structure, as well as the teaching style, was extremely accommodating. I honestly wish every maths course had this assessment structure - the lack of compulsory weekly tests on a dodgy platform (looking at you, 1141/1241) meant there was less work to get through weekly and less overall panic. This allowed us to work at our own pace and focus on learning stuff for what it was, not because it would let us pass tests. The complete lack of rigidity, especially with tutorials (and the aforementioned assessment structure, oh my goodness) made for the most comfortable learning environment I've been in since I started uni. Really.  The fact that this was employed without a hand-holding feeling was probably the best part of the course.

I know I should stick to strictly course-related things here, but it's impossible to skate over how good the lecturers were; they really knew their stuff and ran through their content well enough that most questions were about extensions to course content and not follow-up/clarification questions. They also had actual personality and some epic memes, which was seriously refreshing.

HelpICantThinkOfAName:
Subject Code/Name: ECON3121 - Industrial Organisation/Managerial Economics

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

Assumed Knowledge: ECON2101. I would also recommend taking ECON2112 before this.

Assessment:

25% - Midterm exam. Nothing difficult here, just do your tutorial problems. Done in moodle though, so be careful when putting in your answers.

25% - Group Project. An interesting project where we had to read a research paper, summarise it, analyse it, and then propose a new research question that extended the original research.

50% - Final Exam. Same format as the midterm exam, just do your tutorial problems and you shouldn't have any issue here.

Lecture Recordings?  Yes.

Notes/Materials Available:  Full slides given out.

Lecturer:   Shengyu Li, 3/5.

Year & Trimester of completion: 2021/T1

Difficulty: 1.5/5.

Overall Rating:  3/5.

Your Mark/Grade: 75 D.

Comments: I was kinda bored of this course for most of it if I'm being honest. The first 5 weeks were essentially revision from ECON2112, while the last few were spent extending a few ideas that we'd already touched upon. Not a bad course, but don't expect anything radically different from ECON2112 like I was expecting here.

RuiAce:
Subject Code/Name: MATH3161 - Optimization
Equivalent postgraduate variant: MATH5165. Note that as an honours student, MATH5165 was the variant I took.

Contact Hours: 2x2hr lectures, 1hr tutorial

Assumed Knowledge: 12 UoC of Level 2 maths courses (i.e. 2 courses in Level 2). Must be one of the following:
- (MATH2011 or MATH2111) and (MATH2501 or MATH2601)
- MATH2019 (DN) and MATH2089
- MATH2069 (CR) and MATH2099

Assessment:
- 15% class test
- 20% class test
- 5% assignment
- 60% final exam

Lecture Recordings? Yes, as per usual in COVID times. Also tutorial recordings.

Notes/Materials Available: A fair bit. Lecture slides are relatively condensed compared to other courses like MATH3901. Concise and to the point. (But lack some proofs for pure math minded students.) Tutorials available with solutions. Roughly two past papers per test given, with solutions. Resources are released on a weekly basis. Jeya also makes it clear that some more past papers are available on UNSW library, albeit without solutions. This course also got a digital uplift in 2019, and there are recorded videos for some hard problems, sample class tests, and also proofs of some results assumed in the lectures. Many of the videos are definitely worth watching. And also, for supplementary material, he has some recent research into optimisation made available for the interested student.

Textbook: None. I never felt a textbook was necessary either. But there are some reference books on the course outline.

Lecturer(s): Prof. Jeya Jeyakumar

Year & Trimester of completion: 21 T1

Difficulty: 3/5 for 3161, 3.5/5 for 5165

Overall Rating: 4.5/5 (A little surprisingly. Was honestly contemplating 4/5.)

Your Mark/Grade: 98 HD

Comments:
This course is one of several level 3 applied mathematics courses offered. Well known to be one of the most applicable math courses, optimization is the mathematics of making best decisions. This course is the multivariate generalisation of the introductory, univariate optimization introduced in Year 12. It sees applications in data science, medical research, financial industries and more. Personally, I don't see why any applied maths student would not take this course, and I'd also strongly recommend it for statistics students too. It's way too useful.

The course is understandably a grind to several students; myself included often. Some worthwhile algebraic skill is required, especially once you reach the final topic on optimal control theory. Little mistakes in algebra can cascade into a whole pile of working out based off a previous error, and then being catastrophic.

A lot of resources in this course are valuable, because Jeya seems to excel at using anything that he's taught in class. I lost my marks in class test 1 for basically not reviewing the subtle things in the lectures (which didn't appear at all in tutorials and past papers). After learning my lesson, I always studied the lectures to the finest detail. I felt it paid off a lot, and saved me a ton of marks. (Honestly that would be my advice: STUDY THE LECTURES.)

Tutorials and past papers are still valuable though. You start to see that many of Jeya's question styles do repeat a little. Also, videos I watched included the hard tutorial problems, the sample class tests, and the optimal control problems. (Without a doubt, optimal control is the most annoying topic, and takes loads of practice to get good at. Also, optimal control is basically guaranteed to be the last thing in the final exam - Jeya makes no attempt to hide this.)

It does also help that the topics build on one another (even the numerical methods topics). I felt it made studying for this course more 'fluent', for lack of a better word?

My only small peeve was the 5165 assignment felt too much effort for a mere 5%. Fun questions, but wish there was more reward to it for the heavy load of effort required.

Following on from a previous review for this course though, I highly agree with the need to be careful in your answers. Jeya is quite lenient in many aspects of marking, but attention to detail is definitely not one of them. Ensure that you've covered every bit of detail in your responses, just as he does so in the lectures and the tutorials.

RuiAce:
Subject Code/Name: MATH3361 - Stochastic Differential Equations: Theory, Applications, and Numerical Methods
Equivalent postgraduate variant: MATH5361. Note that as an honours student, MATH5165 was the variant I took.

Contact Hours: 2hr lecture, 1hr laboratory, 1hr tutorial

Assumed Knowledge: Specified for 3361 are one of:
- MATH2011, MATH2111, MATH2018 (DN), MATH2019(DN), MATH2069(DN). and
- MATH2801, MATH2901, MATH2089(DN), MATH2099(DN)
No formal prerequisite specified for 5361, but should be about the same as the above.

Assessment:
- 15% written test, split into two parts (I think each part was basically 7.5%)
- 15% lab test
- 20% assignment
- 50% final exam

Lecture Recordings? Yes, as per usual in COVID times. Also tutorial recordings.

Notes/Materials Available: Lectures, tutorials, and lab exercises gradually uploaded. Pretty standard for maths courses. Some introductory MATLAB notes are also provided.

Textbook: No prescribed as far as I could tell. Reference books can be found in the course outline.

Lecturer(s): Prof. Thanh Tran

Year & Trimester of completion: 21 T1

Difficulty: 4/5 (mostly due to finals; up until then it's really just 2/5 at most)

Overall Rating: 4.5/5

Your Mark/Grade: 96 HD

Comments:
This course is one of several level 3 applied mathematics courses offered. Stochastic DEs address one key drawback from ordinary DEs, that everything has to be deterministic. Take something simple like modelling stocks, and inevitably there will be what looks like randomness in the stock price. Another could be just basic population growth, which we can't assume in general must be deterministic like exponential growth.

Admittedly, this course felt extremely comfortably relaxing prior to the finals. It felt like another where although the lectures were full on, the assessments were much friendlier and not demanding at all to do, provided you carefully studied everything. There's only 2 hours of lecture a week, so there's also less content to be absorbed.

Coding is done in MATLAB, and was assessed in the lab test and assignment. Generally speaking, it suffices to carefully study all the labs (but in particular, the numerical methods). MATLAB documentation was allowed for these, from memory. For the most part, Thanh cares about your code doing the right thing. (Missing a minor code optimisation was probably allowed.)

It also helped a lot that for minor errors, Thanh would point them out, yet not penalise. Helps understanding, and isn't harsh on the marks either.

You should definitely have some minor stats background before coming into this course (MATH2801/2901 is definitely enough). It is stochastic differential equations after all. MATH2121/2221 experience is not required (ordinary differential equations) at all, but if you took it then you might understand the Karhunen-Loeve expansion a little more quickly than others.

The difficulty pretty much all came from the final exam. Which had some relaxing questions early on, but gradually stemmed into what felt like a watered down analysis paper. Certainly felt harder this year than in other years to do, and was a little stressful. I later verified that 5361 had a couple extra questions on top of 3361. (Which was unfortunate, because I did struggle more in the 5361 only questions.)

Knowing how to use inequalities was helpful for this year's exam in particular. The inequalities were provided for you in the exam (i.e. less memorisation), but it was often hard puzzling where to use it. (Whereas for the past paper, it felt more like an ability to manipulate limits and sums.) These are all pretty common tools for proofs in analysis though. Not sure how many students would've fought through all of it.

An observation is that numerical methods seemed to appear more in the coding component, whilst everything else (elementary stochastic analysis, stochastic integrals, stochastic DEs) seemed to appear more in the theory. But still, numerical methods was also in the theory. KL expansions and GFE methods destroyed my head way more than pre-cursor topics, but that was not surprising. It does also mean that you should pay closer attention to those topics, for the finals.

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