UNSW Courses
here is an assortment of various courses from UNSW.
I only write brief notes on some courses here – very brief – only enumerations of Prescribed texts and maybe some term offering information.
There will not be any copyrighted material found here for you to pirate. Only a repackaging of some course-outline accessible information.
Math
MATH2801
Introduction to Mathematical Statistics
MATH2901
- All of Statistics, by Wasserman
- Mathematical Statistics & Data Analysis by Rice
- A first look at rigorous probability theory, by Rosenthal
MATH3371 - Numerical Linear Algebra
- Peter J. Olver and Chehrzad Shakiban, Applied Linear Algebra, Second Edition, Springer 2018.
(Digital copy P 512.5/244)
- Lloyd N. Trefethen and David Bau, Numerical Linear Algebra, SIAM Publications, 1997. (Hard
copy, Library Level 4, P512.5/128 A)
MATH5605 - Functional Analysis
2026 T2 outline attached. 2 lectures/week (Mon 2h + Fri 2h seminar), no tutorials. Short Assignment 1 10%, take-home Midterm 20%, Short Assignment 2 10%, Final Exam 60% (2h in-person). Planned 27T2 in the MStat sequence.
MATH5825 - Measure, Integration and Probability
- G.B. Folland, Real Analysis, Wiley 1984.
- W. Rudin, Real and complex analysis. McGraw-Hill, 1987.
- P. Billingsley, Probability and Measure, P519.1/492
- P.R. Halmos, Measure theory, P517.52/24
- W. Rudin, Functional analysis. McGraw-Hill, 1991.
- H.L. Royden, Real Analysis, McMillan, 1978.
- J.L. Doob, Measure theory, P517.52/171
- A.N. Kolmogorov and S.V. Fomin, Introductory Real Analysis, Dover, 1975.
| Weeks | Topic |
|---|---|
| 1 | Problems of the Riemann integral. Lebesgue’s “problem of measure” in Rd |
| 2 | Abstract measure theory - σ-algebras, measurable sets, measures, outer measures, Lebesgue measure and its properties, completion of measures. |
| 3 | Measurable functions, approximation by simple functions |
| 4 | Lebesgue integral, Monotone Convergence Theorem, Dominated Convergence Theorem, co-incidence of Lebesgue and Riemann integral for Riemann integrable functions |
| 5 | Probabilistic language. Random variables, expectation |
| 7 | Lp spaces |
| 8 | Signed measures, Hahn decomposition theorem, Jordan decompositions, absolute continuity of measures, Lebesgue decomposition, Radon–Nikodym Theorem, Radon–Nikodym derivatives, chain rule |
| 9 | Weak convergence of measures. Convergence in measure |
| 10 | Conditional expectations. Martingales. Martingale Convergence Theorems |
MATH5975 - Introduction to Stochastic Analysis
- S. Shreve, Stochastic Calculus for Finance II, Continuous Time Models, Springer 2004.
- Ioannis Karatzas and Stephen Shreve: Brownian Motion and Stochastic Calculus. Springer, Berlin Heidelberg New York, 1988.
- Bernt Oksendal : Stochastic Differential Equations: An Introduction with Applications (Universitext), 6 edition, Springer, Berlin Heidelberg New York,
$4,980. (for international students it is $7,470!)
MATH5905 - Statistical Inference
- Casella, G. and Berger, R. Statistical Inference. Second Edition, Brooks/Cole (2001). This is the recommended textbook.
- Young, G. and Smith, R. Essentials of Statistical Inference. Cambridge University Press (2005).
- A.W. van der Vaart. Asymptotic Statistics. Cambridge University Press (1998).
- Wasserman, L., All of Nonparametric Statistics. Springer (2006).
- DasGupta, A. Asymptotic Theory of Statistics and Probability. Springer (2008).
a core course for the Stats Masters. allegedly helpful for Time Series. 5k for the course (as is seeming more and more normal); the hecs kids pay 10 times less.
this course will be expensive for me, because naturally I will be interested in purchasing as many of the textbooks as I can to both supplement my own study / know where to look for the rest of my lifetime.
MATH5835 - Advanced Stochastic Processes
offered:
this course seems largely like a formality. I already have the math3901 notes printed so I can reference those as necessary.
- Foundations of Modern Probability, by Olav Kallenberg (any edition)
- A Course in Probability, by Kai Lai Chung (third edition)
- Stochastic Processes: From Applications to Theory, by Pierre Del Moral and Spiridon Penev
MATH5960 - Bayesian Inference and Computation
- Bayesian Data Analysis (second edition), A Gelman, J Carlin, H Stern and D Rubin, Chapman and Hall http://www.stat.columbia.edu/~gelman/book/
- Bayes and Empirical Bayes Methods for Data Analysis (second edition), B.P.Carlin and T.A.Louis, Chapman and Hall
- Markov Chain Monte Carlo - Stochastic simulation for Bayesian inference, D. Gammerman, Chapman and Hall
- Bayesian Inference, 2nd Edition, Vol 2B of “Kendall’s Advanced Theory of Statistics,” A. O’Hagan and J. J. Forster (2004), Arnold, London
MATH3901 - (Higher) Probability and Stochastic Processes
MATH5845 - Time Series
I’ve thieved some resources from coursehero. They are attached.
MATH5925 - Project
MATH5005 - Project A
Solo project, 27T2 in the MStat sequence. No course outline is published in the UNSW course-outlines system for 2023–2026 (all terms/delivery modes probed 2026-07-20); the generic project outline lives under MATH5925.
MATH5006 - Project B
Solo project, 27T3. Same as MATH5005: no published outline exists (checked 2026-07-20); see MATH5925 for the generic project structure.
MATH5806 - Applied Regression Analysis
2026 T2 outline attached (in the course DIR). 2 lectures/week (Mon 2h + Tue 1h) + 1 tutorial. Quiz Wk4 10% (in-class MCQ), Assignment 15% (Wk9, R), Mid-session Test Wk7 20%, Final Exam 55% (2h).
MATH5881 - Statistical Machine Learning
MATH5855 - Multivariate Analysis
Offered T3; planned 26T3 in the MStat sequence. Latest outline is 2025 T3 (no 2026 outline published yet — attached, rendered from the official course-outlines API; the genuine static 2022 T3 school PDF is attached too). Assignment 1 Wk4 10%, Assignment 2 Wk9 10%, Mid-Term Test Wk7 20%, Final Exam 60%. R/SAS computing.
MATH5805 - Special Topics in Statistics (Extreme Value Theory)
EVT has no standalone code at UNSW — it runs under the rotating special-topics slot MATH5805; the 2025 T1 offering was “A Modern Introduction to Extreme Value Theory” (outline attached, rendered from the official API). Because the topic rotates, confirm the 27T1 offering actually is EVT before enrolling. Quiz ~Wk4 10%, Class Test 15%, Assignment 20%, Final Exam 55%. Not offered every year. The 27T1 outline is not yet released — the notebook template assumes the same structure as MATH5806 (2 lec + 1 tut/week, quiz Wk4, 1 assignment, midterm, final) until it is.
MATH5916 - Survival Analysis
Offered T1; planned 27T1 in the MStat sequence. 2026 T1 outline attached (rendered from the official API). 1 lecture + 1 tutorial per teaching week (weeks 1–5, 7–10). Assignment 1 10%, Assignment 2 10%, Mid-Term Test ~Wk7 20%, Final Exam 60%. Heavy use of R. Note: NOT MATH5885 (that is a different course).
Computer Science
COMP6713 - Natural Language Processing
okay sorry - I guess there are a couple cs courses in here too:
- Daniel Jurafsky and James H. Martin. 2025. Speech and Language Processing: An Introduction to Natural Language Processing, Computational Linguistics, and Speech Recognition with Language Models, 3rd edition. Online manuscript released January 12, 2025. https://web.stanford.edu/~jurafsky/slp3.
- Pushpak Bhattacharyya and Aditya Joshi. 2023. Natural Language Processing. Kindle Edition released December 2023. Wiley. https://www.amazon.com.au/Natural-Language-ProcessingPushpak-Bhattacharyya-ebook/dp/B0CR64RX4T
I’ve decided not to take this course because it will cost me 7.6K AUD, which is something that I thought much less about in my under-grad.
COMP9418 - Advanced Machine Learning
Prescribed Book:
- Modelling and Reasoning with Bayesian Networks. Adnan Darwiche. Cambridge. 2009
Recommended Resources:
- Probabilistic Graphical Models: Principles and Techniques. Daphne Koller and Nir Friedman. MIT Press. 2009
- Probabilistic Graphical Models: Principles and Applications. Luis Enrique Sucar. Springer. 2015.
- Bayesian Reasoning and Machine Learning. David Barber. Cambridge University Press. 2012.
- Machine Learning: A Probabilistic Perspective. Kevin P. Murphy. MIT Press. 2012.
- Pattern recognition and machine learning. Christopher M. Bishop. Springer, 2006.
Finance
FINS5513 - Investments and Portfolio Selection
FINS5535 - Derivatives and Risk Management Techniques
FINS5536 - Fixed Income Securities and Interest Rate Derivatives
Backlinks (2)
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Originally the AI suffix stood for archived intellect, however these days it has concretised to becoming an Augmenting Infrastructure — a place from which to branch out in many directions.
Within this site you will find self-contained material in the form of project posts and blog posts, but also external links 1 to other work – my own as well as not.