Lecture Schedule
Date  Topic  Materials 
Jan.12  Introduction. Linear models.  Lecture 1 slides Bishop, Sec. 1.1, 3.1, 3.2 (or equivalent) If you need to catch up on the math, a brief probability review and linear algebra and matrix calculus review from Stanford University; also, Bishop appendix B,C. 
Jan.14  More on linear models. Overfitting. Regularization.  Lecture 2 slides Bishop, Sec. 3.1, 3.2 
Jan.19  More on Bayesian and maximum likelihood fitting  Lecture 3 slides 
Jan.21  Bayesian regression. Logistic regression  Lecture 4 slides 
Jan.26  More on logistic regression. Kernels.  Lecture 5 slides

Jan.28  More on kernels. Supoort vector machines  Lecture 6 slides 
Feb. 2  Active learning 
Lecture 7 slides

Feb. 4  Learning with structured data. Introduction to graphical models via mixture models.  Lecture 8 slides

Feb. 9  Guest lecture (TBA)  Lecture 9 slides 
Feb.11  Guest lecture (TBA)  Lecture 10 slides 
Feb.16  Representational power of directed graphical models. Inference methods  Lecture 11 slides 
Feb.18  Learning methods for graphical models. Latent variables.  Lecture 12 slides 
Feb.23  Undirected graphical models. Representational power.  Lecture 13 slides 
Feb.25  Inference and learning in undirected graphical models  Lecture 14 slides 
Mar. 8  Boltzmann machines. Deep belief networks  Lecture 15 slides 
Mar.10  Unsupervised learning: a latent variable perspective  Lecture 16 slides 
Mar.15  Dimensionality reduction: PCA, kernel PCA, autoencoders.  Lecture 17 slides 
Mar.17  More on autoencoders. Variational inference  Lecture 18 slides 
Mar.22  Time series analysis. Latent variable models for time series analysis.  Lecture 19 slides 
Mar.24  Spectral methods for time series.  Lecture 20 slides

Mar.29  Nonparametric methods for time series (and other structured) data  Lecture 21 slides

Mar.31  Inclass midterm exam 

Apr. 5  Monte Carlo vs temporaldifference methods for time series.  Lecture 22 slides 
Apr. 7  Reinforcement learning. Markov Decision Processes and Bellman equations  Lecture 23 slides 
Apr.12  Function approximation methods for reinforcement learning  Lecture 24 slides 
Apr.14  The problem of optimal control. Explorationexploitation tradeoff. Valuebased and policybased methods.  Lecture 25 slides 