Lecture Schedule
Date  Topic  Materials 
Jan.6  Introduction  Lecture 1 slides Bishop, Sec. 1.1 If you need to catch up on the math, a brief probability review and linear algebra review from Stanford University 
Jan. 8  More on linear models. Regularization.  Lecture 2 slides Bishop, Sec. 3.1, 3.2 
Jan.13  Maximum likelihood and Bayesian learning  Lecture 3 slides Bishop, Sec. 3.1, 3.3 
Jan. 15  Nonlinear methods: Kernels  Lecture 4 slides 
Jan.20  More on nonlinear methods. Optimization approaches.  
Jan. 22  The notion of margin. Maxmargin methods  
Jan.27  Gaussian processes  
Jan. 29  PCA  Lecture 8 slides

Feb. 3  Computational learning theory  Lecture 9 slides 
Feb. 5  More computational learning theory  See slides from above 
Feb.10  Mixture models  Lecture 11 slides 
Feb.12  Graphical models: Bayes nets  Lecture 12 slides 
Feb.17  Exact inference for graphical models  
Feb.19  More on inference methods for graphical models. Learning in directed graphical models  Lecture 14 slides 
Feb.24  Approximate inference methods  
Feb.26  Maximum likelihood learning and Bayesian learning for graphical models.  
Mar.10  Learning with latent variables. EM  
Mar.12  More on unsupervised learning  
Mar.17  Deep belief methods  
Mar.19  More on deep models  
Mar.24  Analyzing temporal and sequence data  
Mar.26  Inclass midterm exam. Covers lectures up to March 19  
Mar.31  Spectral methods for time series  
Apr. 2  Temporaldifference learning  
Apr. 7  More on reinforcement learning  
Apr. 9  More on reinforcement learning. Wrapup 