logistic regression developed honestly as a GLM, from MATH5806: bernoulli in canonical form, the logit link, IRLS from scratch in R, deviance and odds-ratio inference, separation, and the machine-learning reading as corollary.
Statistics
2026-09-27
| # | Course Code | Title | Offered | Prerequisites | Term | Type | Textbook | Notes |
|---|---|---|---|---|---|---|---|---|
| 1. | COMP6713 | Natural Language Processing | T1 | MATH1081,9444 | 26T1 | Elective | na | |
| 2. | FINS5513 | Investments and Portfolio Selection | T1,2,3 | 8750 program | 26T1 | Elective | na | |
| 3. | FINS5536 | Fixed Income Securities and Interest Rate Derivatives | T2 | 5513 | 26T2 | Elective | na | pricing, hedging, risk management. options, futures and swaps (int rate derivs) |
| 4. | MATH5856 | Introduction to Statistics and Statistical Computations | T2 | 26T2 | Elective | na | recommended for 5905 | |
| 6. | MATH5960 | Bayesian Inference and Computation | T3 | 2801/2901 | 26T3 | Elective | ||
| 7. | MATH5825 | Measure, Integration and Probability | T3 | U5705 | 26T3 | Elective | na | implicit prereq for 5835 |
| 8. | MATH5905 | Statistical Inference | T1,2,3 | U5846,U5856 | 27T1 | Core | na | |
| 9. | COMP9518 | Advanced Machine Learning | T2 | 9517 | 27T2 | Elective | na | |
| 10. | MATH5845 | Time Series | T2 | 27T2 | Elective | na | ||
| 11. | MATH5855 | Multivariate Analysis | T3 | 27T3 | Elective | na | ||
| 12. | MATH5835 | Advanced Stochastic Processes | T1 | U5825 | 28T1 | Core | na | Difficult. Requires an understanding of Real Analysis and Measure Theory |
| 13. | MATH5806 | Applied Regression Analysis | T2 | 28T2 | Elective | na | splines, poisson / binomial regression | |
| 14. | MATH5925 | Project (12uoc) | T1,2,3 | 36UoC | 28T2 | Core | na |
course pages
Per-course write-ups — admin, textbooks, and links into my notes and solutions — for the courses I have completed:
the regression shelf: ordinary least squares, regularised and locally-weighted variants, logistic regression, and the generalised linear model theory that unifies them — estimation, inference, and worked implementations.
Non-descriptive frisbee stats
A computer vision model that takes in streamed games and outputs a player statistic that factors in non-descriptive events — i.e. giving the correct call at the correct time, or poaching in the lane to force a bad throw.
I expect this to be trained using a transformer and written in Python. It is inspired by Andrew Wood’s analytical Ultimate dream.
Lab 2
The Chapter 2 lab is a tour of the language itself – vectors, matrices, indexing, plotting, and loading a data set. What follows is that tour, split into the sections the book uses.
Basic Commands
Vectors are built with c() (concatenate), and both <- and = assign. Arithmetic is element-wise, so operands must share a length – reassigning x to length 3 lets it line up with y:
Chapter 1: Getting Started
mean(abs(rnorm(100))) # generate 100 N(0,1) variates, take abs, then mean
rnorm(10)
Chapter 2: Vectors
Chapter 3: Matrices and Arrays
Chapter 4: Lists
Chapter 5: Data Frames
Chapter 6: Factors and Tables
Chapter 7: R Programming Structures
Chapter 8: Doing Math and Simulations in R
Chapter 9: Object-Oriented Prgoramming
Chapter 10: Input/Output
Chapter 11: String Manipulation
Chapter 12: Graphics
Chapter 13: Debugging
Chapter 14: Performance Enhancement: Speed and Memory
Chapter 15: Interfacing R to Other Languages
Chapter 16: Parallel R
Pierre Lafaye de Micheaux, Rémy Drouilhet and Benoît Liquet, The R Software: Fundamentals of Programming and Statistical Analysis, Springer Statistics and Computing 40, 2013 (translated from the French Le logiciel R). Part I introduces R and the book’s data sets; Part II covers the language (data structures, import/export, manipulation and functions, documentation, plots, programming, sessions); Part III applies it to matrices and optimisation, descriptive statistics, random variables and simulation, confidence intervals and tests, linear regression and ANOVA. Chapters 3 to 15 each end with exercises and a practical worksheet.
Worked solutions in R to all 78 exercises in the fourth edition of Dobson & Barnett's An Introduction to Generalized Linear Models — exponential family theory, estimation and inference, normal linear models, binomial and Poisson regression, contingency tables, survival analysis, clustered and longitudinal data, and Bayesian analysis with MCMC.