Textbooks

Math at Undergrad and Grad Level

Calculus

  • Thomas’ Calculus
  • Stewart’s Calculus
  • Lax & Terrell - Calculus and Multivariable Calculus with Applications

Linear Algebra

  • Lay, Lay, & McDonald - Linear Algebra and Its Applications
  • Trefethen & Bau - Numerical Linear Algebra
  • Sheldon Axler - Linear Algebra Done Right
  • Friedberg, Insel, Spence - Linear Algebra

Real and Functional Analysis

  • Steven Krantz – Real Analysis and Foundations
  • Walter B. Rudin - Principles of Mathematical Analysis
  • Thomas Apostol - Mathematical Analysis
  • Luigi Ambrosio, Giuseppe Da Prato and Andrea Mennucci - Introduction to Measure Theory and Integration
  • Donald Cohn - Measure Theory
  • Vladimir I. Bogachev - Measure Theory, Vol. 1 and 2
  • Bogachev, Smolyanov - Real and Functional Analysis
  • Gerald B. Folland - Real Analysis: Modern Techniques and Their Applications
  • Haïm Brezis - Functional Analysis, Sobolev Spaces and Partial Differential Equations

Algebra

  • John B. Fraleigh - A First Course in Abstract Algebra
  • Dummit & Foote - Abstract Algebra

Advanced Undergraduate Probability

  • Anirban DasGupta - Fundamentals of Probability: A First Course
  • Grimmett & Welsh - Probability: An Introduction
  • Gregory Lawler - Introduction to Stochastic Processes
  • James Norris - Markov Chains

Graduate Probability

  • Grimmett & Stirzaker - Probability and Random Processes
  • Jean-François Le Gall - Measure Theory, Probability, and Stochastic Processes
  • Rick Durrett - Probability: Theory and Examples
  • Patrick Billingsley - Probability and Measure
  • Richard M. Dudley - Real Analysis and Probability
  • Olav Kallenberg - Foundations of Modern Probability

Markov Chains on countable state space

  • Levin, Peres, & Wilmer - Markov Chains and Mixing Times

Brownian Motion and Stochastic Analysis

  • Jean-François Le Gall - Brownian Motion, Martingales, and Stochastic Calculus
  • Mörters & Peres - Brownian Motion
  • Revuz & Yor - Continuous Martingales and Brownian Motion
  • Karatzas & Shreve - Brownian Motion and Stochastic Calculus

Ergodic Theory

  • Patrick Billingsley - Ergodic Theory and Information
  • Einsiedler & Ward - Ergodic Theory, with a view towards Number Theory

High-Dimensional Probability

  • Roman Vershynin - High-Dimensional Probability: An Introduction with Applications to Data Science
  • Ramon van Handel - Lecture Notes for APC 550: Probability in High Dimension

Statistics

  • A.W. van der Vaart - Asymptotic Statistics
  • Martin Wainwright - High-dimensional Statistics: A Non-asymptotic Viewpoint

Information Theory

  • Cover and Thomas - Elements of Information Theory
  • Polyanskiy and Wu - Information Theory: from Coding to Learning

Game Theory

  • Thomas Ferguson - A Course in Game Theory
  • Karlin & Peres - Game Theory, Alive

Probability Research Texts

  • High-Dimensional Probability: an Introduction with Applications to Data Science (Roman Vershynin)

  • APC550 High-Dimensional Probability (Ramon van Handel)

  • Concentration of Measure Phenomenon (Michel Ledoux)

  • Probability in Banach Spaces (Michel Ledoux and Michel Talagrand)

  • Concentration Inequalities: a Nonasymptotic Theory of Independence (Stéphane Boucheron, Gábor Lugosi, and Pascal Massart)

  • Concentration Inequalities and Model Selection (Saint-Flour 2003, Pascal Massart)

  • Random Fields and Geometry (Robert Adler and Jonathan Taylor)

  • Spectral Analysis of Large Dimensional Random Matrices (Zhidong Bai and Jack Silverstein)

  • Percolation (Geoffrey Grimmett)

  • 50 Years of First-Passage Percolation (Antonio Auffinger, Michael Damron, and Jack Hanson)

  • Proceedings of Symposia in Applied Mathematics Vol.79: Random Growth Models (AMS Short Course January 2–3, 2017; edited by Michael Damron, Firas Rassoul-Agha, and Timo Seppäläinen)

  • Statistical Mechanics of Lattice Systems: a Concrete Mathematical Introduction (Sacha Friedli and Yvan Velenik)

  • A Course on Large Deviations with an Introduction to Gibbs Measures (GSM 162; Firas Rassoul-Agha and Timo Seppäläinen)

  • Analysis and Geometry of Markov Diffusion Operators (Dominique Bakry, Ivan Gentil, and Michel Ledoux)