Textbook

High-Dimensional Data Analysis: Wright & Ma Textbook

John Wright and Yi Ma's graduate textbook on high-dimensional data analysis — sparse models, low-rank models, and deep networks unified under a single mathematical framework. Adopted at Berkeley, Columbia, UIUC, Tsinghua. Published Cambridge University Press 2022.

Mathematical Foundations of Reinforcement Learning

Mathematical Foundations of Reinforcement Learning by Shiyu Zhao is an open-access textbook covering RL theory rigorously — Bellman equations, value functions, policy gradient methods — with a mathematical depth missing from most applied RL courses. Free on GitHub.

Biological Circuit Design: Caltech Textbook

A web-based textbook on biological circuit design by Michael Elowitz and Justin Bois (Caltech) — covering genetic circuits, feedback mechanisms, oscillatory dynamics, and developmental patterning with Python exercises. The quantitative foundation for understanding living systems as engineerable circuits.

Notes on Theory of Distributed Systems

James Aspnes's freely-distributed lecture notes on the theory of distributed systems, covering fault tolerance, consensus, synchrony models, and randomized algorithms. A rigorous but accessible graduate reference that grounds distributed computing in formal models.

Stochastic Processes and Simulations: An ML Perspective

Stochastic Processes and Simulations: A Machine Learning Perspective by Vincent Granville — covers point processes, Poisson-binomial processes, GPU clustering, and extreme value theory with Python code. Bridges probability theory and ML's simulation-heavy approach to uncertainty.

Machine Learning — Tom Mitchell (1997)

Tom Mitchell's 1997 Machine Learning textbook from CMU — the foundational academic reference that defined the field's core concepts before deep learning. Still assigned in graduate courses for its clear treatment of decision trees, Bayesian methods, and the canonical formal definition of machine learning.

Pen & Paper Exercises in Machine Learning

Michael Gutmann's (Edinburgh) collection of pen-and-paper exercises covering the mathematical foundations of machine learning — linear algebra, optimization, graphical models, density estimation, and classification. Designed to build mathematical fluency that coding-first courses skip, using derivation rather than implementation as the primary learning mode.

Linear Algebra Done Wrong

Sergei Treil's free textbook on linear algebra, written for mathematically advanced undergrads and deliberately structured around abstract reasoning rather than computational cookbook recipes. The title is a joke — it's done 'wrong' in the sense of prioritizing theory and proof over drill.

Proofs, Arguments, and Zero-Knowledge

Reinforcement Learning: An Introduction

Sutton and Barto's canonical RL textbook (2nd ed., MIT Press 2018) — the field's foundational reference, covering MDPs through deep RL in a unified framework. Essential reading before any other RL material; everything else builds on the ideas introduced here.

Algorithms for Decision Making (2-column edition)

A two-column or second-edition variant of the MIT Press textbook by Mykel Kochenderfer covering decision-making under uncertainty, from MDPs and POMDPs through reinforcement learning and multi-agent systems. This copy predates the primary 2023-01 vault entry and may represent an earlier draft or reformatted version.

Deep Learning with PyTorch

Manning's 2020 practical guide to deep learning with PyTorch by Stevens, Antiga, and Viehmann, covering tensors through CNNs, RNNs, generative models, and production deployment. The go-to book for practitioners who want to understand PyTorch from first principles rather than copy-paste patterns.

Machine Learning in Finance: From Theory to Practice

Springer 2020 textbook by Dixon, Halperin, and Bilokon bridging ML theory and quantitative finance practice — covering supervised learning, NLP for financial texts, RL for trading, and deep learning for derivatives pricing. The most rigorous academic treatment of ML applied to finance.

Data Mining for Co-Location Patterns

Guoqing Zhou's CRC Press book on co-location pattern mining, a spatial data mining problem concerned with finding features that frequently appear together in geographic proximity. A specialized but valuable topic for anyone working with geospatial data analysis.

Relativity: A Modern Primer

A modern introductory treatment of relativity — likely covering both special and general relativity with contemporary notation and pedagogical framing. Useful as a mathematically careful primer that bridges high school physics and graduate-level treatments like Misner, Thorne & Wheeler.

An Introduction to Statistical Learning (2nd Edition)

The second edition of James, Witten, Hastie, and Tibshirani's canonical intro-level statistical learning textbook, updated in 2021 to include deep learning, survival analysis, and multiple testing. It sits between undergraduate statistics and the more demanding Elements of Statistical Learning — the best entry point for practitioners who want rigorous but accessible ML foundations.

Machine Learning from Scratch

Machine Learning from Scratch is a free online book deriving seven core ML algorithms from first principles — linear regression, logistic regression, naive Bayes, decision trees, ensembles, and neural networks. Mathematically rigorous, aimed at practitioners who want to understand how algorithms work mechanistically.

Speech and Language Processing (Jurafsky & Martin)

The free online draft of Jurafsky and Martin's 'Speech and Language Processing' — the canonical NLP textbook, covering everything from regex to transformers, now in its 3rd edition. Freely available online in draft form and unusually well-written for a technical textbook.

Statistical Inference for Everyone

Statistical Inference for Everyone (SIE) is a free introductory statistics textbook by Brian Blais that teaches through examples and probability rather than formulas. Recommended as a gentler alternative to frequentist-heavy introductory texts.

Datashaping Analytics Book (ABbook5)

A self-published analytics/statistics PDF by Vincent Granville (datashaping.com) — one of his data science book resources available as a free download circa 2013. Granville was an early data science community builder who self-published extensively on applied analytics.

Jeff Erickson's Algorithms Course Materials

Jeff Erickson's algorithms course materials from UIUC — lecture notes covering data structures, graph algorithms, dynamic programming, and computational geometry. Freely available and widely regarded as among the clearest algorithm teaching materials available online.

Mining of Massive Datasets (Stanford)

The Stanford textbook by Rajaraman and Ullman on algorithms for mining massive datasets — locality-sensitive hashing, PageRank, collaborative filtering, stream algorithms, and more. Freely available online and a standard reference for large-scale data algorithms.

Aho/Ullman Foundations of Computer Science

The classic undergraduate CS foundations textbook by Alfred Aho and Jeffrey Ullman, freely available from Stanford. Covers data structures, algorithms, automata, and the mathematical foundations underpinning computer science as a discipline.

Algorithms — Dasgupta, Papadimitriou, Vazirani

The free PDF of 'Algorithms' by Dasgupta, Papadimitriou, and Vazirani — Berkeley's undergraduate algorithms textbook. Unusual for a CS textbook in being readable, mathematically rigorous, and freely available from the authors.