Machine Learning Books and Tutorials
General Overviews
Books
- Information Theory, Inference, and Learning Algorithms (David MacKay, 2003)
- Bayesian Reasoning and Machine Learning (David Barber, 2012)
- Machine Learning: A Probabilistic Perspective (Kevin Murphy, 2013)
- The Elements of Statistical Learning (Trevor Hastie, Robert Tibshirani and Jerome Friedman, 2009)
- Pattern Recognition and Machine Learning (Christopher M. Bishop, 2006)
- Computer Age Statistical Inference: Algorithms, Evidence and Data Science (Bradley Efron and Trevor Hastie, 2017)
- Introduction to Applied Linear Algebra – Vectors, Matrices, and Least Squares (Stephen Boyd and Lieven Vandenberghe, 2018)
- Mathematics for Machine Learning (Marc Peter Deisenroth, A. Aldo Faisal and Cheng Soon Ong, 2020)
- Statistical Learning Glossary (Tom Minka)
Online Lectures
Some Specialized Topics
Books
- Bayesian Data Analysis (Andrew Gelman, John Carlin, Hal Stern, David Dunson, Aki Vehtari, and Donald Rubin, 2008)
- Gaussian Processes for Machine Learning (Carl E. Rasmussen and Christopher K. I. Williams, 2006)
- Reinforcement Learning: An Introduction (Richard S. Sutton and Andrew G. Barto, 2018)
- Probabilistic Graphical Models (Daphne Koller and Nir Friedman, 2009)
- Deep Learning (Ian Goodfellow, Yoshua Bengio and Aaron Courville, 2016)
Online Tutorials
Robotics Books
London/UK Networks