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:

David Barber’s book “Bayesian Reasoning and Machine Learning”

Rasmussen and Williams’ book “Gaussian Processes for Machine Learning”

David Mackay’s book “Information Theory, Inference, and Learning Algorithms”

A Graphical Models, Exponential Families, and Variational Inference

The Elements of Statistical Learning

A list of books recommended by Mike Jordan

Bayesian Nonparametrics

Lecture notes on Bayesian nonparametrics by Peter Orbanz

Reproducing Kernel Hilbert Spaces

Arthur Gretton’s course for the Gatsby Unit on RKHS theory

Bayesian Optimisation

A Tutorial on Bayesian Optimization by Eric Brochu, Vlad M. Cora and Nando de Freitas

Reinforcement Learning

Video lectures by David Silver

Deep Learning

Ian Goodfellow, Yoshua Bengio and Aaron Courville’s textbook