Statistics and machine learning books with full text freely available online:
- Understanding Deep Learning by Simon J.D. Prince (2023).
- Learning Theory from First Principles by Francis Bach (2023).
- Probabilistic Machine Learning: Advanced Topics by Kevin P. Murphy (2023).
- Probabilistic Machine Learning: An Introduction by Kevin P. Murphy (2022).
- An Introduction to Statistical Learning with Applications in R (2nd edition) by Gareth James, Daniela Witten, Trevor Hastie and Robert Tibshirani (2021)
- Advanced Data Analysis from an Elementary Point of View by Cosma Rohilla Shalizi (2021).
- Patterns, Predictions, and Actions: A Story about Machine Learning by M. Hardt and B. Recht (2021).
- Core Statistics by Simon Wood (2015)
- Bayesian Data Analysis (3rd edition) by Andrew Gelman et al. (2013)
- Introduction to Statistical Thought by Michael L. Lavine (2013)
- Bayesian Reasoning and Machine Learning by David Barber (2012).
- Machine Learning: A Probabilistic Perspective by Kevin P. Murphy (2012).
- Principles of Uncertainty by Joseph Kadane (2011).
- The Elements of Statistical Learning (2nd edition) by Trevor Hastie, Robert Tibshirani, and Jerome Friedman (2009)
- Pattern Recognition and Machine Learning by Christopher Bishop (2006).
- Gaussian Processes for Machine Learning by Carl Edward Rasmussen and Christopher K. I. Williams (2006).
- Information Theory, Inference, and Learning Algorithms (video lectures) by David J. C. MacKay (2003).
- Fragmentary version of Probability Theory: The Logic of Science by Edwin T. Jaynes (1994).
My lecture notes:
- Multivariate Statistics and Machine Learning (MATH38161) by Korbinian Strimmer.
- Statistics 2: Likelihood and Bayes (MATH27720) by Korbinian Strimmer.
- Statistical Methods: Likelihood, Bayes and Regression (MATH20802) by Korbinian Strimmer.
Other links:
- Feynman Lectures of Physics.
- Motion Mountain - The Adventure of Physics by Christoph Schiller.
- Online resources and books concerning statistical distributions and matrices.
- Random: Probability, Mathematical Statistics, Stochastic Processes by Kyle Siegrist.