Lecture Schedule
Date  Topic  Materials 
Jan.6  Introduction. Linear models.  Lecture 1 slides Bishop, Sec. 1.1 If you need to catch up on the math, a brief probability review and linear algebra and matrix calculus review from Stanford University 
Jan. 8  More on linear models. Overfitting. Regularization.  Lecture 2 slides 
Jan.13  More on fitting machine learning models. Maximum likelihood and Bayesian perspective.  Lecture 3 slides Bishop, Sec. 3.1, 3.3 
Jan. 15  Nonlinear methods: Kernels  Lecture 4 slides 
Jan.20  Optimization in the dual space. Maximum margin classification.  Lecture 5 slides

Jan. 22  Computational learning theory (part 1)  Lecture 6 slides 
Jan.27  Computational learning theory. Active learning 
Lecture 7 slides
Lecture 7 slides 
Jan. 29  Mor eon active learning. COLT for regression  Lecture 8 slides

Feb. 3  Learning with structured data. Introduction to graphical models via mizture models.  Lecture 9 slides 
Feb. 5  Representational power of directed graphical models. Inference methods  Lecture 10 slides 
Feb.10  Learning methods for graphical models. Latent variables.  Lecture 11 slides 
Feb.12  Undirected graphical models. Representational power.  Lecture 12 slides 
Feb.17  Inference and learning in undirected graphical models  Lecture 13 slides 
Feb.19  Boltzmann machines. Deep belief networks  Lecture 14 slides 
Feb.24  Unsupervised learning: a latent variable perspective  Lecture 15 slides 
Feb.26  Dimensionality reduction: PCA, kernel PCA, autoencoders.  Lecture 16 slides 
Mar.10  Topic modelling  Lecture 17 slides 
Mar.12  Inclass midterm. Covers lectures until the end of February. 

Mar. 17  Time series analysis. Latent variable models for time series analysis.  Lecture 18 slides 
Mar.19  Spectral methods for time series.  Lecture 19 slides

Mar.24  Monte Carlo vs temporaldifference methods for time series.  Lecture 20 slides 
Mar.26  Nonparametric methods for time series (and other structured) data  Lecture 21 slides 
Mar.31  Reinforcement learning. Markov Decision Processes and Bellman equations  Lecture 22 slides 
Apr. 2  Function approximation methods for reinforcement learning  Lecture 23 slides 
Apr. 7  The problem of optimal control. Explorationexploitation tradeoff. Valuebased methods.  Lecture 24 slides 
Apr. 9  Policy search methods  Lecture 25 slides 