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. Vershynin. High-dimensional Probability, 2019.

R. Durrett. Elementary Probability for Applications, 2009,

R. Durrett. Probability: Theory and Examples, 2019.

R. Durrett. Essentials of Stochastic Processes, 2016.

Data Science/Statistics

R. A. Irizzary, Introduction to Data Science, 2024.

A. Spirling and A. Jones-Rooy. Data Science for Everyone, 2024.

J. Leskovec, A. Rajaraman and J. Ullman. Mining of Massive Datasets, 2019.

C. Fernandez-Granda. Mathematical Tools for Data Science, 2024.

S. Holmes, W. Huber. Modern Statistics for Modern Biology, 2019.

B. Efron, T. Hastie. Computer Age Statistical Inference, 2016.

A. Gelman et al. Regression and other stories, 2022.

Statistical/Machine Learning

G. James et al. Introduction to Statistical Learning, 2023.

T. Hastie, R. Tibshirani, J. Friedman. Elements of Statistical Learning, 2009.

K.P. Murphy. Probabilistic Machine Learning: An Introduction, 2022.

K.P. Murphy. Probabilistic Machine Learning: Advanced Topics, 2023. 

B. Boehmke, B. Greenwell. Hands-on Machine Learning with R, 2020.

M. J. Zaki and W. Meira. Data Mining and Machine Learning, 2020. 

A. Lindholm et al. Machine Learning: A First Course for Engineers and Scientists, 2022.

M. Mohri et al. Foundations of Machine Learning, 2018.

F. Bach. Learning Theory from first principles, 2023.

M.P. Deisenroth et al. Mathematics for Machine Learning, 2020.

S. Shalev-Shwartz, S. Ben-David. Understanding Machine Learning: From Theory to Algorithms, 2016.

Deep Learning

Y. LeCunn, A. Canziani. Deep Learning, 2024.

C.M. Boshop. Deep Learning, 2024.

F. Fleuret. The Little Book of Deep Learning, 2023.

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.