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. Logistic regression  Lecture 3 slides 
Jan.17  More on logistic regression. Introduction to kernels.  Lecture 4 slides 
Jan.19  More on kernels. Introduction to Support vector machines.  Lecture 5 slides Bishop, Sec. 4.3 > 
Jan.24  More on kernels. Supoort vector machines  Lecture 6 slides Bishop, Sec. 5.1, 5.2, 5.3, 5.4, 5.5 
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  Hidden Markov Models: Inference and Learning  Lecture 10 slides 
Postponed  Learning methods for graphical models. Latent variables.  Lecture 10 slides 
Feb. 9  More on learning in directed graphical models  Lecture 11 slides 
Feb.14  No class (watch Tibshirani invited talk at NIPS)  
Feb.16  Undirected graphical models  Lecture 12 slides 
Feb.21  Dimensionality reduction: PCA, kernel PCA, LLE, MDS...  Lecture 13 slides Bishop 12.1, 12.3 (or equivalent) Locally Linear Embeddings (optional) 
Feb.23  TBD  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 