This is a collection of notes that I and others have found useful. Under construction!

Kudos to Matej Balog, Nilesh Tripuraneni for recommendations.

 

General courses and books for Bayesian / Frequentist Machine Learning

Slides for the Gatsby Unit courses: http://www.gatsby.ucl.ac.uk/teaching/courses/ml1-2015.html

David Barber’s book “Bayesian Reasoning and Machine Learning” http://web4.cs.ucl.ac.uk/staff/D.Barber/pmwiki/pmwiki.php?n=Brml.Online

Rasmussen and Williams’ book “Gaussian Processes for Machine Learning” http://www.gaussianprocess.org/gpml/

David Mackay’s book “Information Theory, Inference, and Learning Algorithms” http://www.inference.phy.cam.ac.uk/itprnn/book.pdf

A Graphical Models, Exponential Families, and Variational Inference https://www.eecs.berkeley.edu/~wainwrig/Papers/WaiJor08_FTML.pdf

The Elements of Statistical Learning http://www.web.stanford.edu/~hastie/local.ftp/Springer/ESLII_print10.pdf

A list of books recommended by Mike Jordan http://www.statsblogs.com/2014/12/30/machine-learning-books-suggested-by-michael-i-jordan-from-berkeley/

Bayesian Nonparametrics

Lecture notes on Bayesian nonparametrics by Peter Orbanz http://stat.columbia.edu/~porbanz/papers/porbanz_BNP_draft.pdf

Reproducing Kernel Hilbert Spaces

Arthur Gretton’s course for the Gatsby Unit on RKHS theory http://www.gatsby.ucl.ac.uk/~gretton/coursefiles/rkhscourse.html

Bayesian Optimisation

A Tutorial on Bayesian Optimization by Eric Brochu, Vlad M. Cora and Nando de Freitas
http://arxiv.org/pdf/1012.2599v1.pdf

Reinforcement Learning

Video lectures by David Silver
https://www.youtube.com/playlist?list=PL5X3mDkKaJrL42i_jhE4N-p6E2Ol62Ofa

Deep Learning

Ian Goodfellow, Yoshua Bengio and Aaron Courville’s textbook http://www.deeplearningbook.org/