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 reader available from the Metcalf Copy Center.
Date  Topic  Primary Readings  Additional Readings  Materials 

1/27  Course Overview  MLaPP: 1.1  slides  
2/01  Classification Probability: What and Why? 
MLaPP: 1.2 MLaPP: 2.2 
slides  
2/03  Discrete Random Variables Maximum Likelihood & Naive Bayes 
MLaPP: 2.3, 2.6 MLaPP: 1.4.3, 3.1, 3.3 
slides  
2/08  Categorical Maximum Likelihood Continuous Random Variables Bayesian Inference 
MLaPP: 3.33.5 MLaPP: 2.4, 2.5 MLaPP: 4.14.2 
slides  
2/10  Gaussian Maximum Likelihood Categorical Bayesian Estimation Decision Theory, ROC Curves 
MLaPP: 3.2 MLaPP: 1.8, 4.5, 4.6 MLaPP: 8.18.3 
slides  
2/15  Decision Theory, Precision & Recall Model Selection, Crossvalidation 
MLaPP: 8.2, 8.3 MLaPP: 1.8, 4.8, 4.9 
slides  
2/17  Multivariate Gaussian Distributions Discriminant Analysis 
MLaPP: 5.15.3 MLaPP: 1.4.1 
slides  
2/22  Brown Holiday: No Lecture  
2/24  Linear Regression & Least Squares Bayesian Linear Regression 
MLaPP: 1.3, 3.7 MLaPP: 5.35.5 
slides  
3/01  Bayesian Regression & Prediction Bayesian Model Selection Logistic Regression 
MLaPP: 5.5 MLaPP: 1.8.5, 4.8 MLaPP: 1.2.71.2.13 
slides  
3/03  Logistic Regression Gradient Descent, Newton's Method 
MLaPP: 3.8, 14.214.3 MLaPP: 10.110.3 
slides  
3/08  Iteratively Reweighted Least Squares Stochastic Gradient Descent 
MLaPP: 10.3 MLaPP: 10.2 
slides  
3/10  Exponential Families Generalized Linear Models Robust Linear Regression 
MLaPP: 33.5 MLaPP: 14.514.6 MLaPP: 1.3.4, 13.3 
slides  
3/15  Midterm Exam: In Class  
3/17  Binary Feature Selection & Search L1 Regularization & Sparsity 
MLaPP: 15.2 MLaPP: 15.3 
slides  
3/22  L1 Optimization & Sparsity Kernel Methods Gaussian Process Regression 
MLaPP: 15.4 MLaPP: 16.116.2 MLaPP: 16.3 
slides  
3/24  Gaussian Process Classification, GLMs Laplace Approximations 
MLaPP: 16.316.4 MLaPP: 11.2 
slides  
3/29  Spring Break: No Lecture  
3/31  Spring Break: No Lecture  
4/05  Perceptron Algorithm GP Laplace Approximations 
MLaPP: 10.2.4 MLaPP: 16.4 
slides  
4/07  Margins & Support Vector Machines Clustering & KMeans Algorithm 
MLaPP: 17.4 MLaPP: 1.5.1, 19.1, 19.2 
slides  
4/12  Probabilistic Mixture Models EM for Gaussian Mixtures 
MLaPP: 1.5.1, 19.3 MLaPP: 10.4.110.4.2 
slides  
4/14  Expectation Maximization Algorithm  MLaPP: 10.4  slides  
4/19  Principal Components Analysis Factor Analysis & Probabilistic PCA 
MLaPP: 1.5.2, 20.220.3  slides  
4/21  EM for Factor Analysis & PCA  MLaPP: 20.220.3  EM for PPCA  slides 
4/26  Topic Models Hidden Markov Models 
MLaPP: 21.2, 21.4 MLaPP: 6.8, 22.2 
slides topic models 

4/28  IPP Symposium: No Lecture  
5/03  Hidden Markov Models Monte Carlo, MCMC, Gibbs Samplers 
MLaPP: 22.222.3 MLaPP: 12.2, 12.5, 12.6 
slides  
5/05  Gibbs Samplers Directed Graphical Models 
MLaPP: 12.6, 12.9 MLaPP: 6.2, 6.3, 6.6, 6.7 
slides  
5/10  Final Exam Review Session  slides  
5/12  Graduate Project Presentations 
Recitations
Date  Topic  Presenters  Readings  Materials 

2/03  Matlab Tutorial  Jason & Soumya  Matlab  
2/10  Continuous Random Variables Bayesian Estimation 
Jason  MLaPP: 4.5, 33  Matlab notes 
2/17  Linear Algebra Tutorial  Soumya  MLaPP: 32  notes 
2/24  Multivariate Gaussian Distributions  Jason  MLaPP: 5  notes 
3/03  Continuous Optimization  Soumya  MLaPP: 10.110.3, 31.4  Matlab slides 
3/10  Midterm Review Session  Jason & Soumya  MLaPP: 15, 8, 10  slides 
3/17  No Recitation  
3/24  Lagrange Multipliers  Jason  MLaPP: 31.5, 31.8  notes 
3/31  No Recitation  
4/07  Gaussian Processes  Soumya  MLaPP: 16  notes slides 
4/14  EM Algorithm  Jason  MLaPP: 10.4  notes 
4/21  Markov Chains  Soumya  MLaPP: 2.8, 31.7  notes 
4/28  Dynamic Programming & HMMs  Jason  MLaPP: 22.2  notes slides 
5/05  No Recitation 