Postgrad Guide – JC Schoeman

Skripsie Guide – JC Schoeman


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

Control Theory – Steve Brunton

Convex Optimisation – Stephen Boyd

Linear Algebra – Gilbert Strang

Machine Learning – Herman Kamper

Neural Networks – Hugo Larochelle

Probabilistic Graphical Models – Daphne Koller

Reinforcement Learning – David Silver

Robotics – Pieter Abbeel