The bookshelf of machine learning and data science
Probability
J. Blitzstein, J. Hwang. Introduction to Probability, 2019.
S.H. Chan. Introduction to Probability for Data Science, 2021.
R. Durrett. Elementary Probability for Applications, 2009,
R. Durrett. Probability: Theory and Examples, 2019.
R. Durrett. Essentials of Stochastic Processes, 2016.
C. Fernandez-Granda. Probability and Statistics for Data Science, 2024.
R. Vershynin. High-dimensional Probability, 2019.
S. Pal and T. Mesikepp. Finite Markov Chains and Monte-Carlo Methods: An Undergraduate Introduction, 2025.
Data Science/Statistics
B. Efron, T. Hastie. Computer Age Statistical Inference, 2016.
A. Gelman et al. Regression and other stories, 2022.
S. Holmes, W. Huber. Modern Statistics for Modern Biology, 2019.
R. A. Irizzary, Introduction to Data Science, 2024.
J. Leskovec, A. Rajaraman and J. Ullman. Mining of Massive Datasets, 2019.
S. Roch. Mathematical Methods in Data Science, 2025.
A. Spirling and A. Jones-Rooy. Data Science for Everyone, 2024.
Statistical/Machine Learning
F. Bach. Learning Theory from first principles, 2023.
B. Boehmke, B. Greenwell. Hands-on Machine Learning with R, 2020.
M.P. Deisenroth et al. Mathematics for Machine Learning, 2020.
G. James et al. Introduction to Statistical Learning, 2023.
T. Hastie, R. Tibshirani, J. Friedman. Elements of Statistical Learning, 2009.
A. Lindholm et al. Machine Learning: A First Course for Engineers and Scientists, 2022.
M. Mohri et al. Foundations of Machine Learning, 2018.
K.P. Murphy. Probabilistic Machine Learning: An Introduction, 2022.
K.P. Murphy. Probabilistic Machine Learning: Advanced Topics, 2023.
S. Shalev-Shwartz, S. Ben-David. Understanding Machine Learning: From Theory to Algorithms, 2016.
L. Younes. Introduction to Machine Learning, 2025.
Deep Learning
C.M. Boshop. Deep Learning, 2024.
M. Bronstein et al. Geometric Deep Learning, 2025.
F. Fleuret. The Little Book of Deep Learning, 2023.
Y. LeCunn, A. Canziani. Deep Learning, 2024.
B. Liquet et al. The Mathematical Engineering of Deep Learning, 2024.
S.J.D. Prince. Understanding Deep Learning, 2023.
S. Scardapane. Alice's Adventures in a Differentiable Wonderland, 2024.
Miscellanea
A. Owen. Monte Carlo theory, methods and examples, 2013.
P. L’Ecuyer. Stochastic Simulation and Monte Carlo Methods, 2024.
N. Boumal. An introduction to Optimization on smooth manifolds, 2023.
R.J. Hyndman and G. Athanasopoulos. Forecasting: Principles and Practice, 2021.
G. Peyré, M. Cuturi. Computational Optimal Transport, 2018.
C. Bouveyron et al. Model-Based Clustering and Classification, 2022.
H. Wickham et al. R for Data Science, 2023.
S. Barocas et al. Fairness and Machine Learning, 2023.