Lecture Schedule
Date  Topic  Materials 
Jan. 5  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.10  More on linear models. Overfitting. Regularization.  Lecture 2 slides Bishop, Sec. 3.1, 3.2 
Jan.12  More on Bayesian and maximum likelihood fitting  Lecture 3 slides 
Jan.17  Logistic regression  Lecture 4 slides 
Jan.19  Introduction to kernels. Kernelbased logistic regression.  Lecture 5 slides

Jan.24  More on kernels. Supoort vector machines  Lecture 6 slides 
Jan.26  Active learning 
Lecture 7 slides

Jan.31  Learning with structured data. Introduction to graphical models via mixture models.  Lecture 8 slides

Feb. 2  Representational power of directed graphical models. Inference methods  Lecture 9 slides 
Feb. 7  Learning methods for graphical models. Latent variables.  Lecture 10 slides 
Feb. 9  Undirected graphical models. representational power  Lecture 11 slides 
Feb.14  Inference and learning in undirected graphical models  Lecture 12 slides 
Feb.16  Boltzmann machines Deep belief networks  Lecture 13 slides 
Feb.21  More on deep learning (supervised models)  Lecture 14 slides 
Feb.23  Generative adversarial networks  Lecture 15 slides 
Mar. 7  Time series analysis. Latent variable models for time series analysis.  Lecture 16 slides 
Mar. 9  Spectral methods for time series.  Lecture 17 slides 
Mar.14  Other types of time series analysis  Lecture 18 slides 
Mar.16  Unsupervised learning: a latent variable perspective  Lecture 19 slides 
Mar.21  Dimensionality reduction: PCA, kernel PCA, autoencoders.  Lecture 20 slides

Mar.23  Autoencoders. Variational inference  Lecture 21 slides

Mar.28  Inclass midterm exam 

Mar.30  Computational learning theory  Lecture 22 slides 
Apr. 4  More on computational learning theory  Lecture 23 slides 
Apr. 6  Reinforcement learning for prediction  Lecture 24 slides 
Apr.11  Reinforcement learning for control  Lecture 25 slides 