Worked solutions to all 373 exercises in Blum, Hopcroft & Kannan's Foundations of Data Science: high-dimensional geometry, SVD, random walks. Free PDF.
Probability
2026-10-22
Worked solutions to all 624 exercises in Casella & Berger's Statistical Inference (2nd edition) — probability theory, transformations and expectations, common families of distributions, multiple random variables, properties of a random sample, principles of data reduction, point estimation, hypothesis testing, interval estimation, asymptotic evaluations, and the analysis of variance and regression.
Worked solutions to all 215 exercises in Bayesian Data Analysis (3rd edition) by Gelman, Carlin, Stern, Dunson, Vehtari and Rubin — probability and inference, single- and multi-parameter models, hierarchical models, model checking and comparison, MCMC and Hamiltonian Monte Carlo, variational inference, regression and generalized linear models, and nonparametric Bayesian models.
Bayesian Data Analysis (3rd edition, CRC Press, 2013) by Gelman, Carlin, Stern, Dunson, Vehtari and Rubin — the standard graduate reference for Bayesian modelling: single- and multi-parameter models, hierarchical models, model checking and comparison, posterior computation from MCMC to HMC and variational inference, regression, and nonparametric models.
Worked solutions to all 215 exercises live at Solutions to Gelman et al.’s Bayesian Data Analysis.
Statistical Inference (2nd edition, Duxbury, 2002) by George Casella and Roger L. Berger — the classical graduate text on the theory of statistics: probability, transformations and expectations, families of distributions, multiple random variables, properties of random samples, principles of data reduction, point estimation, hypothesis testing, interval estimation, asymptotics, ANOVA and regression.
Worked solutions to all 624 exercises live at Solutions to Casella & Berger’s Statistical Inference.
Worked solutions to all 203 exercises in Jeffrey S. Rosenthal's A First Look at Rigorous Probability Theory (2nd edition) — probability triples and extension theorems, random variables, expected values, inequalities and convergence, distributions, gambling games, discrete Markov chains, weak convergence, characteristic functions, decomposition of laws, conditional expectation, and martingales.
bayesian networks from first principles — the chain rule, conditional probability tables, d-separation and its valve intuition, markov blankets, i-maps, naive bayes classifiers and gaussian networks.
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.
| # | 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 probabilistic graphical models section — bayesian and markov networks, exact and approximate inference, sequence models, and learning from data. built from unsw's comp9418.