Worked solutions to all 373 end-of-chapter exercises in Foundations of Data Science by Avrim Blum, John Hopcroft and Ravindran Kannan — high-dimensional geometry and concentration, the SVD and best-fit subspaces, random walks and Markov chains, machine learning theory, streaming and sketching, clustering, random graphs, topic models and NMF, wavelets, and the mathematical appendix.
Solutions
Worked solutions to all 236 end-of-chapter exercises in Algorithm Design by Jon Kleinberg and Éva Tardos — stable matching, asymptotic analysis, graph search, greedy algorithms, divide and conquer, dynamic programming, network flow, NP-completeness, PSPACE, fixed-parameter and approximation algorithms, local search, and randomized algorithms, each with an algorithm, a correctness proof and a running-time bound.
Worked solutions to all 164 end-of-chapter exercises and 57 worksheet practicals (353 numbered worksheet questions) in The R Software: Fundamentals of Programming and Statistical Analysis (Springer, 2013) — data structures, import and export, data manipulation, plots, programming, sessions, matrix algebra and optimisation, descriptive statistics, simulation, confidence intervals and tests, linear regression and ANOVA — with executed R code and committed output throughout.
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.
Worked solutions to all 467 exercises in Stephen Abbott's Understanding Analysis (2nd edition) — the real numbers and completeness, sequences and series, the topology of R, functional limits and continuity, the derivative, sequences and series of functions, the Riemann integral, and the additional topics chapter from metric spaces to Fourier series.
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.
Worked solutions to all 342 exercises in Adnan Darwiche's Modeling and Reasoning with Bayesian Networks — propositional logic, probability calculus, building Bayesian networks, exact inference by variable elimination, factor elimination and conditioning, jointrees and graph decomposition, MPE and MAP, complexity, compilation, belief propagation, sampling, sensitivity analysis, and parameter/structure learning.
Worked solutions to all 1062 problems in Royden & Fitzpatrick's Real Analysis (4th edition) — Lebesgue measure and integration for functions of a single real variable, differentiation and integration, the Lp spaces, metric and topological spaces, Banach and Hilbert spaces, and general measure and integration theory.
A student's guide to MATH5806 at UNSW: generalised linear models in R, the Dobson & Barnett textbook, assessment breakdown (quiz, R assignment, mid-session, final), plus free notes and fully worked GLM solutions.