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
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
MATH5005/5006 - Project Solo
MATH5806 - Applied Regression Analysis
MATH5881 - Statistical Machine Learning
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.