AAPI Heritage Month
Mathematics for Machine Learning by Marc Peter DeisenrothThe Elements of Statistical Learning by Trevor HastieDeep Learning by Ian GoodfellowInformation Theory, Inference, and Learning Algorithms by David J.C. MacKayPattern Recognition and Machine Learning by Christopher M. Bishop
Mathematics for Machine Learning
8th out of 120 books — 13 voters
Structure and Interpretation of Computer Programs by Harold AbelsonIntroduction to the Theory of Computation by Michael SipserLearn You a Haskell for Great Good! by Miran LipovačaThe Little Schemer by Daniel P. FriedmanFunctional Programming in Scala by Rúnar Bjarnason
Computer Science Year 3 (MCSL)
6th out of 56 books — 9 voters

Computational Complexity by Sanjeev AroraQuantum Computation and Quantum Information by Michael A. NielsenIntroduction to the Theory of Computation by Michael SipserThe Nature of Computation by Cristopher MooreIntroduction to Algorithms by Thomas H. Cormen
Theoretical Computer Science (MMath)
13th out of 57 books — 6 voters
All of Statistics by Larry WassermanConvex Optimization by Stephen BoydDeep Learning by Ian GoodfellowReinforcement Learning by Richard S. SuttonNumerical Algorithms by Justin Solomon
Computer-Math
2nd out of 17 books — 3 voters

The Mathematical Theory of Communication by Claude ShannonPrinciples of Mathematical Analysis by Walter RudinCalculus by Michael SpivakCalculus on Manifolds by Michael SpivakA Survey of Minimal Surfaces by Robert Osserman
MIT Mathematics syllabus books
26th out of 105 books — 5 voters
Introduction to Linear Algebra by Gilbert StrangIn All Likelihood by Yudi PawitanConvex Optimization by Stephen BoydOptimization by Vector Space Methods by David G. LuenbergerCausal Inference in Statistics by Judea Pearl
Sridhar Mahadevan's 20 math books for Machine Learning
3rd out of 21 books — 1 voter

A Probabilistic Theory of Pattern Recognition by Luc DevroyeFoundations of Machine Learning by Mehryar MohriUnderstanding Machine Learning by Shai Shalev-ShwartzGeometric Modeling in Probability and Statistics by Ovidiu CalinConcentration Inequalities by Stéphane Boucheron
Machine Learning for the Pure Mathematician
50th out of 54 books — 3 voters
Statistical Inference by George CasellaA Course in Large Sample Theory by Thomas S. FergusonElements of Large-Sample Theory by Erich L. LehmannAsymptotic Statistics by A.W. van der VaartIntroduction to Nonparametric Estimation by Alexandre B. Tsybakov
Machine learning books suggested by prof. Michael I. Jordan from Berkeley
12th out of 16 books — 1 voter

Reinforcement Learning by Richard S. SuttonMarkov Decision Processes by Martin L. PutermanAlgorithms for Reinforcement Learning by Csaba SzepesvariConvex Optimization by Stephen BoydAlgorithms for Reinforcement Learning by Csaba Szepesvari
Reinforcement Learning
4th out of 18 books — 3 voters
Linear Algebra and Its Applications by Gilbert StrangConvex Optimization by Stephen BoydIntroduction to Probability Models by Sheldon M. RossA First Course in Probability by Sheldon M. RossR in a Nutshell by Joseph Adler
Recommended Reading from Doing Data Science
2nd out of 14 books — 1 voter

Calculus by Michael SpivakAlgebra by Paolo AluffiVisual Complex Analysis by Tristan NeedhamA Book of Abstract Algebra by Charles C. PinterPrinciples of Mathematical Analysis by Walter Rudin
Undergraduate Mathematics
421st out of 571 books — 37 voters
Numerical Optimization by Jorge NocedalNetwork Science by Albert-László BarabásiDynamic Programming by Richard E. BellmanNetworks by M.E.J. NewmanDynamic Programming and Markov Processes by Ronald A. Howard
Operational Research (MMath)
6th out of 7 books — 7 voters


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