Machine Learning Books and Tutorials

General overviews

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, 2019) A great source for all sorts of tutorials
Online ML Crash Course (Google)
Online course on Mathematics for Machine Learning (Coursera)

Statistical Learning Glossary by Tom Minka

Some specialized topics

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, 1998)
Probabilistic Graphical Models (Daphne Koller and Nir Friedman, 2009)
Deep Learning (Ian Goodfellow, Yoshua Bengio and Aaron Courville, 2016)

Expectation Propagation (Lecture by Tom Minka)
Markov Chain Monte Carlo (Lecture by Iain Murray)
Gaussian Processes (Lecture by Carl Edward Rasmussen)

Robotics Books

Probabilistic Robotics (Sebastian Thrun, Wolfram Burgard, and Dieter Fox, 2006)
A Survey on Policy Search for Robotics (Marc Deisenroth, Gerhard Neumann and Jan Peters, 2013)

London/UK Networks

Imperial College Machine Learning Initiative
Imperial College Robotics Forum
Imperial College Data Science Institute

London Robotics Network

Leverhulme Centre for the Future of Intelligence


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