MATH3371: Numerical Linear Algebra

- 3x quizzes, worth 5% each and delivered via Mobius (not as bad as you think, but still, ugh)
- Class test, worth 20%
- Assignment, worth 15%
- Final exam, worth the remaining 50%

One of the following:
- MATH2501/2601
- MATH2019 with at least a DN
- MATH2099 with CR

Basically, any course beyond 1st year which covers linear algebra. Knowing some programming is an implicit requirement here too: you can use either MATLAB, Julia or Python for the course.

This is a brand new course, so no one really knew what to expect, but I decided to take a chance on it since it seemed like an interesting mix of maths and computing. Having done so, I feel this was a decent pick. The next run in 2023 will probably feel a bit better to those taking it, since the course content was still sort of in development during the term, and they’ll have feedback from our cohort to use for further changes. To be objective though, the course was a little bit unpolished this term.

The focus of this course is, naturally, numerical linear algebra. Linear algebra is everywhere, and often the method you learn in the theoretical linear algebra courses for doing various things (e.g. finding the eigenvalues of a matrix) is too slow, totally impractical or prone to precision errors in the real world. As well as seeing the derivation/justification for these more practical methods of performing linear algebra, you also analyse how their computational costs scale and, where possible, how different methods compare. While the material in the first half of the course is pretty chill, things start picking up after flex week: the numerical analysis in the accuracy and reliability topic is no joke, and conjugate gradient methods can be a bit tough to get your head around at first. Probably the most confusing part of this course though is the sudden inclusion of a small “machine learning” topic on SVMs at the end, which felt very out of place due to its notable lack of any actual linear algebra (instead, it was basically all nonlinear optimisation).
Given that the course is inherently computational in nature, part of the course work involves programming. The lab tasks and assignment are intended to reinforce this in a hands-on way, while the rest of the course dealt with the theory aspects. The labs weren’t assessed directly, and I didn’t really see the point of them until the final exam, because you were basically required to use numerical computation to answer one question (which made me regret not doing all of the labs; I gave up in probably week 4).

3x 1 hour lecture, 1x 1 hour tutorial, 1x 1 hour lab class

3.5/5

Yes, on Blackboard Collaborate.

AProf. William McLean, Dr. Quoc Le Gia

Full detail course notes are written, but the slides are also provided. The notes are a bit more terse than the slides, so for some topics I found it was actually nicer to read the slides instead.

4/5

No textbook required, and none recommended either.

22T1

88 HD