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 Bishop Sec. 3.3, 4.3 (or equivalent) 
Jan.17  More on logistic regression. Introduction to kernels.  Lecture 4 slides Bishop, Sec. 6.1, 6.2 
Jan.19  More on kernels. Introduction to Support vector machines.  Lecture 5 slides Bishop, Sec. 7.1 
Jan.24  More on kernels. Supoort vector machines  Lecture 6 slides Bishop, Sec. 7.1 
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  Same slide deck as last lecture 
Feb. 7  Hidden Markov Models: Inference and Learning  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  Lecture 13 slides Bishop 12.1, 12.3 (or equivalent) Locally Linear Embeddings (optional) 
Feb.23  Nonlinear dimensionality reduction: autoencoders  Lecture 15 slides Quoc Le tutorial on backpropagation and tutorial on autoencoders, convnets and recurrent nets (first part) 
Mar. 7  Spectral methods for time series.  Lecture 16 slides (thanks to B. Balle, A. Quattoni and X. Carrreras) Machine Learning paper 
Mar. 9  Learning dynamical systems: Bayesain updating, Kalman filters and friends  Lecture 17 slides 
Mar.14  Recurrent Neural Networks  Lecture 18 slides Quoc Le tutorial on backpropagation and tutorial on autoencoders, conv nets and deep nets (last part) 
Mar.16  Method of moments, latent variable models, and tensors  Lecture 19 slides JMLR paper 
Mar.21  Recap for midterm 
Solutions to 2016 midterm> (disucssed in class)

Mar.23  More recap  Lecture 21 slides

Mar.28  Inclass midterm exam 
Some examples of midteurmstyle questions with solutions Midterm from 2016 
Mar.30  Generatice Adversarial Networks  Lecture 22 slides 
Apr. 4  Computational learning theory  Lecture 23 slides

Apr. 6  Reinforcement learning for prediction  Lecture 24 slides 
Apr.11  Reinforcement learning for control  Lecture 25 slides 