URL: here
Instructors: Andrew Ng, John Duchi
I: Introduction
01) Supervised Learning, Discriminative Algorithms
II: Supervised learning
02) Generative Algorithms
03) Support Vector Machines
III: Learning theory
04) Learning Theory
05) Regularization and Model Selection
06) Online Learning and the Perceptron Algorithm
IV: Unsupervised learning
07) Unsupervised Learning, k-means clustering
08) Mixture of Gaussians
09) The EM Algorithm
10) Factor Analysis
11) Principal Components Analysis
12) Independent Components Analysis
V: Reinforcement learning and control
13) Reinforcement Learning and Control
Others: Papers
Scalable Learning of Non-Decomposable Objectives