Postgrad Guide – JC Schoeman
Skripsie Guide – JC Schoeman
Linear Algebra – Gilbert Strang
Neural Networks – Hugo Larochelle
Probabilistic Graphical Models – Daphne Koller
Reinforcement Learning – David Silver
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