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.

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: 3.3-3.5
MLaPP: 2.4, 2.5
MLaPP: 4.1-4.2
slides
2/10 Gaussian Maximum Likelihood
Categorical Bayesian Estimation
Decision Theory, ROC Curves
MLaPP: 3.2
MLaPP: 1.8, 4.5, 4.6
MLaPP: 8.1-8.3
slides
2/15 Decision Theory, Precision & Recall
Model Selection, Cross-validation
MLaPP: 8.2, 8.3
MLaPP: 1.8, 4.8, 4.9
slides
2/17 Multivariate Gaussian Distributions
Discriminant Analysis
MLaPP: 5.1-5.3
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: 5.3-5.5
slides
3/01 Bayesian Regression & Prediction
Bayesian Model Selection
Logistic Regression
MLaPP: 5.5
MLaPP: 1.8.5, 4.8
MLaPP: 1.2.7-1.2.13
slides
3/03 Logistic Regression
MLaPP: 3.8, 14.2-14.3
MLaPP: 10.1-10.3
slides
3/08 Iteratively Reweighted Least Squares
MLaPP: 10.3
MLaPP: 10.2
slides
3/10 Exponential Families
Generalized Linear Models
Robust Linear Regression
MLaPP: 33.5
MLaPP: 14.5-14.6
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: 16.1-16.2
MLaPP: 16.3
slides
3/24 Gaussian Process Classification, GLMs
Laplace Approximations
MLaPP: 16.3-16.4
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 & K-Means 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: 10.4.1-10.4.2
slides
4/14 Expectation Maximization Algorithm MLaPP: 10.4 slides
4/19 Principal Components Analysis
Factor Analysis & Probabilistic PCA
MLaPP: 1.5.2, 20.2-20.3 slides
4/21 EM for Factor Analysis & PCA MLaPP: 20.2-20.3 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: 22.2-22.3
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

# Recitations

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: 10.1-10.3, 31.4 Matlab
slides
3/10 Midterm Review Session Jason & Soumya MLaPP: 1-5, 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