Lectures & Readings
Most course readings are taken from Machine Learning: A Probabilistic Perspective (MLaPP), a draft textbook in preparation by Prof. Kevin Murphy. The first chapter is freely available online. Later chapters will be distributed via a pair of readers available from the Metcalf Copy Center.
The specific schedule of topics and readings below is tentative, and will change as the course progresses.
Date  Topic  Primary Readings  Materials 

1/26  Course Overview  MLaPP: 1.11.3  slides 
1/31  Probability: Discrete random variables Dimensionality & model validation 
MLaPP: 2.12.3 MLaPP: 1.4.11.4.4, 8.3.8 
slides 
2/02  Maximum likelihood & Bayesian learning Naive Bayes classifiers 
MLaPP: 3.13.2 MLaPP: 5.15.2 
slides 
2/07  Probability: Continuous random variables Smoothing: Beta & Dirichlet priors 
MLaPP: 2.4, 2.5.4 MLaPP: 3.33.5 
slides 
2/09  Bayesian decision theory & ROCs Gaussian ML estimation 
MLaPP: 8.18.2, 8.3.4 MLaPP: 1.4.51.4.6 
slides 
2/14  Decision theory & continuous estimation Bayesian model selection Directed graphical models 
MLaPP: 8.2, 10.2 MLaPP: 1.4.71.4.9, 10.3 MLaPP: 9.19.2 
slides 
2/16  Multivariate Gaussian Distributions Gaussian Classification 
MLaPP: 2.5, 4.14.4.2 MLaPP: 5.35.3.1 
slides 
2/21  Brown Holiday: No Lecture  
2/23  Linear Regression & Least Squares Bayesian Linear Regression 
MLaPP: 1.4.51.4.7 MLaPP: 6.26.3 
slides 
2/28  Gaussian Discriminant Analysis Logistic Regression, Probit Regression 
MLaPP: 5.3 MLaPP: 6.4, 7.4 
slides 
3/01  Logistic Regression Gradient Descent, Newton's Method 
MLaPP: 6.4 MLaPP: 6.4 
slides 
3/06  Logistic Regression: ML & MAP Laplace Approximations 
MLaPP: 6.4 MLaPP: 6.5 
slides 
3/08  Exponential Families  MLaPP: 7.17.2  slides 
3/13  Midterm Exam: In Class  
3/15  Generalized Linear Models Robust Linear Regression Binary Feature Selection & Search 
MLaPP: 7.3 MLaPP: 6.2.3 MLaPP: 13.2 
slides 
3/20  L1 Regularization & Sparsity  MLaPP: 13.313.4  slides 
3/22  Online Learning & Perceptrons Kernel Methods 
MLaPP: 6.6 MLaPP: 14.2, 14.4 
slides 
3/27  Spring Break: No Lecture  
3/29  Spring Break: No Lecture  
4/03  Gaussian Process Regression Gaussian Process Classification, GLMs 
MLaPP: 15.1, 15.2 MLaPP: 15.3 
slides 
4/05  Margins & Support Vector Machines  MLaPP: 14.5  slides 
4/10  Clustering & KMeans Algorithm Probabilistic Mixture Models 
MLaPP: 1.3, 11.2 MLaPP: 11.2, 11.3 
slides 
4/12  Graphical Models EM for Mixture Models 
MLaPP: 9.1, 9.2, 9.4 MLaPP: 11.211.4 
slides 
4/17  Expectation Maximization Algorithm  MLaPP: 11.4  slides 
4/19  Principal Components Analysis Factor Analysis & Probabilistic PCA 
MLaPP: 12.112.3  slides 
4/24  EM Algorithm for Factor Analysis & PPCA  MLaPP: 12.112.3  slides 
4/26  Hidden Markov Models Inference & Learning for HMMs 
MLaPP: 17.117.3 MLaPP: 17.417.5 
slides 
5/01  Topic Models Monte Carlo Methods 
MLaPP: 27.3 MLaPP: 23.2, 23.4 
slides 
5/03  MCMC & Gibbs Samplers Continuous State Space Models 
MLaPP: 24.2 MLaPP: 18.118.3, 23.5 
slides 
5/08  Final Exam Review Session  slides 1 slides 2 

5/10  Graduate Project Presentations 
Recitations
Date  Topic  Readings  Materials 

2/02  Matlab Tutorial  YAGTOM  Matlab 
2/09  Continuous Bayesian Estimation  MLaPP: 2.4, 3.3  demo notes 
2/16  Linear Algebra Tutorial  Stanford CS229  notes 
2/23  Multivariate Gaussians, Linear Regression  MLaPP: 4, 6.26.3  demo notes 
3/01  Continuous Optimization  MLaPP: 6.4  demo notes 
3/08  Midterm Review Session  MLaPP: 16, 8, 10  slides 1 slides 2 
3/15  No Recitation  
3/22  Lagrange Multipliers  Klein tutorial  notes 
3/29  No Recitation  
4/05  Kernels  MLaPP: 14  notes 
4/12  EM Algorithm  MLaPP: 11.4  notes 
4/19  Markov Chains  MLaPP: 17.2  Matlab notes 
4/26  Dynamic Programming, HMMs  MLaPP: 17.4  notes 
5/03  No Recitation 