Guides

Postgrad Guide – JC Schoeman

Skripsie Guide – JC Schoeman

Textbooks

Bayesian Reasoning and Machine Learning – David Barber

Deep Learning – Ian Goodfellow, Yoshua Bengio and Aaron Courville

Gaussian Processes for Machine Learning – Carl Rasmussen and Christopher Williams

Probabilistic Robotics – Sebastian Thrun, Wolfram Burgard and Dieter Fox

Reinforcement Learning: An Introduction – Richard Sutton and Andrew Barto

Video Lectures

Linear Algebra – Gilbert Strang

Machine Learning – Herman Kamper

Neural Networks – Hugo Larochelle

Probabilistic Graphical Models – Daphne Koller

Reinforcement Learning – David Silver