Lists of curated books

Links and PDFs when available are provided by the Authors. All materials are copyrighted and protected. We encourage you to buy the final shipped books to support Authors and Publishers.

Textbooks available online with resources (eg., slides/code/exercises):
- "Random Graphs and Complex Networks", Volume 1 & 2 by Remco van der Hofstad, CUP (2024), book home page
- "An introduction to Optimization on smooth manifolds" by Nicolas Boumal (CUP, 2023), book home page
- Computer Age Statistical Inference: Algorithms, Evidence and Data Science by Efron and Hastie, Cambridge University Press, 2016
  PDF
- Statistical Learning with Sparsity: The Lasso and Generalizations by Trevor Hastie, Robert Tibshirani and Martin Wainwright, CRC Press, 2015
- The Shallow and the Deep: A biased introduction to neural networks and old school machine learning, Michael Biehl University of Groningen, 2023, UG Press
- User-friendly Introduction to PAC-Bayes Bounds by Pierre Alquier, Now Publishers, 2024. arXiv, 2023.
- Probability theory: The logic of science by Edwin Thompson Jaynes, CUP 2003. (book was completed by George Larry Bretthorst).
- Geometric Mechanics Part I: Dynamics and Symmetry by Darryl D Holm (personal web page), Imperial College London, 2011
- Artificial Intelligence: Foundations of Computational Agents (3rd Edition) by David L. Poole and Alan K. Mackworth, Cambridge University Press,
- PATTERNS, PREDICTIONS, AND ACTIONS: A story about machine learning, by Moritz Hardt and Benjamin Recht, Princeton University Press, 2022.
- Algorithmic High-Dimensional Robust Statistics by Ilias Diakonikolas and Daniel M. Kane, Cambridge University Press, 2023
  CUP book page
- Distributional Reinforcement Learning by Marc G. Bellemare, Will Dabney and Mark Rowland, MIT Press 2023.
  Book open access PDF
- Reinforcement Learning: An Introduction by Richard S. Sutton and Andrew G. Barto, Second Edition, MIT Press, 2018
  Book web page: http://incompleteideas.net/book/the-book.html
- Machine Learning: A First Course for Engineers and Scientists by A. Lindholm, N. Wahlström, F. Lindsten, and Th. Schön.
  Book web page: http://smlbook.org
  include Neural networks, deep learning, bagging, boosting, Bayesian ML, Gaussian process, generative models, and more!
- Optimization for Modern Data Analysis by Benjamin Recht and Stephen J. Wright.
  Book web page https://people.eecs.berkeley.edu/~brecht/opt4ml_book/
- Mathematical Analysis of Machine Learning Algorithms by Tong Zhang (Hong Kong University of Science and Technology)
  Book web page https://tongzhang-ml.org/lt-book.html
- Bayesian models of perception and action by Wei Ji Ma, Konrad Kording, and Daniel Goldreich
  Book web page https://www.cns.nyu.edu/malab/bayesianbook.html
- by Wright & Ma
  Book https://book-wright-ma.github.io/Book-WM-20210422.pdf
- Bayesian Reasoning and Machine Learning by D. Barber
  Book http://web4.cs.ucl.ac.uk/staff/D.Barber/pmwiki/pmwiki.php?n=Brml.HomePage
- Bayesian Modeling and Computation in Python by Osvaldo Martin , Ravin Kumar and Junpeng Lao
  Book https://bayesiancomputationbook.com/welcome.html
- Solution manual to Bayesian Essentials with R
  Publisher book web page: https://link.springer.com/book/10.1007/978-1-4614-8687-9
  R code package: https://cran.r-project.org/web/packages/bayess/index.html
- Graph Representation Learning by W. L. Hamilton (2023), Morgan & Claypool publisher.
- The Little Book of Deep Learning (about 160 pages) by François Fleuret (2023), University of Geneva, CH.
- Deep Learning: Foundations and Concepts, Chris Bishop AND Hugh Bishop, Springer, 2024
- Understanding Deep Learning (about 540 pages) by Simon J.D. Prince (2023), to be published by MIT Press.
- Algorithms by Jeff Erickson (revised, 2015).
- Mathematics for Machine Learning by Marc Peter Deisenroth, A. Aldo Faisal, and Cheng Soon Ong, Cambridge University Press.
- Computer Vision: Algorithms and Applications by Richard Szeliski, Springer.
- Information theory, inference and learning algorithms by Sir MacKay, combines Information Theory with Machine Learning
  Book web page: https://inference.org.uk/itila/book.html
Statistical inference by minimum divergence:
- Minimum Divergence Methods in Statistical Machine Learning From an Information Geometric Viewpoint by Shinto Eguchi, Osamu Komori, Springer, 2022
- New Developments in Statistical Information Theory Based on Entropy and Divergence Measures by Leandro Pardo (Ed), MDPI Entropy, 2019
  book pdf
- N-distances and Their Applications by Lev B. Klebanov, Karolinum Press, Charles University, 2006.
  book pdf
- The Methods of Distances in the Theory of Probability and Statistics by Svetlozar T. Rachev, Lev B. Klebanov, Stoyan V. Stoyanov, Frank Fabozzi, Springer, 2013
- Statistical Inference: The Minimum Distance Approach by Ayanendranath Basu, Hiroyuki Shioya, Chanseok Park, CRC Press, 2011
- A Probability Metrics Approach to Financial Risk Measures by Svetlozar T. Rachev, Stoyan V. Stoyanov, Frank J. Fabozzi, Wiley-Blackwell, 2011
- Statistical Inference Based on Divergence Measures by Leandro Pardo, CRC Press, 2006
- Model Selection and Multimodel Inference: A Practical Information-Theoretic Approach by Burnham KP, Anderson DR, Springer, 2002 (2nd Edition)
- Probability Metrics and the Stability of Stochastic Models by Svetlozar T. Rachev, Wiley, 1991
- Goodness-of-Fit Statistics for Discrete Multivariate Data by Timothy R.C. Read, Noel A.C. Cressie, Springer Series in Statistics, 1988
  (power divergence statistic)
- Convex Statistical Distances by Friedrich Liese, Igor Vajda, Teubner Press, 1987
High-dimensional probability and statistics (including high-dimensional covariance matrices):
- Tensor Methods in Statistics by Peter McCullagh, second edition, Dover publication, 2018
- Fundamentals of High-Dimensional Statistics: With Exercises and R Labs by Johannes Lederer, Springer 2022
- High-Dimensional Statistics: A Non-Asymptotic Viewpoint by Martin J. Wainwright, Cambridge University Press, 2019
- High-Dimensional Probability: An Introduction with Applications in Data Science by Roman Vershynin, Cambridge University Press, 2018
- Statistics for High-Dimensional Data: Methods, Theory and Applications by Peter Bühlmann, Sara van de Geer, Springer 2011
- High Dimensional Probability, Editors: Ernst Eberlein, Marjorie Hahn, Michel Talagrand, conference proceedings, Springer, 1998
- Analysis of Multivariate and High-Dimensional Data by Inge Koch, Cambridge University Press, 2014
- High-Dimensional Covariance Estimation: With High-Dimensional Data by Mohsen Pourahmadi, Wiley, 2013
- Large Sample Covariance Matrices and High-Dimensional Data Analysis by Jianfeng Yao, Shurong Zheng, Zhidong Bai, Cambridge University Press, 2015 ,
- Introduction to High-Dimensional Statistics By Christophe Giraud, Chapman & Hall, Routledge, 2021 (second edition)
  pdf

Optimal transport (Wasserstein distances, Sinkhorn algorithm/divergence):
- Optimal Transport: Old and New by Cédric Villani (2009), Springer
- Optimal Transport for Applied Mathematicians: Calculus of Variations, PDEs and Modeling by Filippo Santambrogio (2015), Birkhäuser
- Topics in Optimal Transport by Cédric Villani (2003), AMS
- Lectures on Optimal Transport by Luigi Ambrosio, Elia Brué, Daniele Semola (2021), Springer
- An Invitation to Optimal Transport, Wasserstein Distances, and Gradient Flows by Alessio Figalli and Federico Glaudo (2021), EMS Textbooks in mathematics
- Optimal transport methods in economics by Alfred Galichon (2017), Princeton Press
- Model-free Hedging A Martingale Optimal Transport Viewpoint by Pierre Henry-Laborderehttps (2017), Chapman & Hall, CRC
- Sub-Riemannian Geometry and Optimal Transport by Ludovic Rifford (2014), Springer
- Optimal Transport: Theory And Applications, (London Mathematical Society Lecture Note Series, Series Number 413), Yann Ollivier, Hervé Pajot, Cédric Villani(2014)
Algebraic statistics: Field which considers computational algebra, algebraic geometry, and combinatorics among others to tackle problems in statistics.
- Algebraic Statistics by Seth Sullivant, AMS, 2018
- Algebraic Statistics: Computational Commutative Algebra in Statistics by Giovanni Pistone, Eva Riccomagno, and Henry P. Wynn, Chapman & Hall, 2000
- An Introduction to Algebraic Statistics with Tensors by Cristiano Bocci and Luca Chiantini, Springer, 2019
- Markov Bases in Algebraic Statistics by Satoshi Aoki, Hisayuki Hara, and Akimichi Takemura, Springer, 2012
- Semialgebraic Statistics and Latent Tree Models by Piotr Zwiernik, Chapman & Hall, 2016
- Algebraic Statistics for Computational Biology by L. Pachter and B. Sturmfels (Editors), Cambridge University Press, 2005
- Lectures on Algebraic Statistics by Mathias Drton, Bernd Sturmfels, Seth Sullivant, Springer, 2009
- Algebraic Geometry and Statistical Learning Theory by Sumio Watanabe, Cambridge University Press, 2009
Computational topology:
Computational Topology for Data Analysis by Tamal Krishna Dey, Purdue University, Indiana, Yusu Wang, University of California, San Diego
Cambridge University Press, 2022

Affine differential geometry

Su, Buqing. Affine differential geometry. CRC Press, 1983.

Nomizu, Katsumi, and Takeshi Sasaki. Affine differential geometry: geometry of affine immersions. Cambridge university press, 1994.

Li, An-Min, et al. Global affine differential geometry of hypersurfaces. de Gruyter, 2015 (Second edition).

Some recommended biography books

Henri Poincaré: A scientific biography by Jeremy Gray, Princeton University Press, 2012.
Last updated, Frank Nielsen, April 2024.