Lectures
Tuesday & Thursday, 2:30pm-3:50pm, Smith-Buonanno Hall 106.
Most primary readings come from Bishop's Pattern Recognition and Machine Learning (PRML). Available at the Brown Bookstore.
Some readings come from Hastie, Tibshirani, & Friedman's Elements of Statistical Learning (ESL). Available online.
| Date | Topics | Primary Reading | Supplemental Reading | Materials |
|---|---|---|---|---|
| September 10 | Introduction | slides | ||
| September 15 | Classification and Nearest Neighbors | PRML: 1.1, 1.4, 2.5 |
ESL: 2.1-2.3, 2.5 |
slides |
| September 17 | Generative Models and Decision Theory | PRML: 1.2, 1.3, 1.5 |
ESL: 2.4 |
slides |
| September 22 | Directed Graphical Models | PRML: 8.1-8.2 |
slides | |
| September 24 | Frequentist Learning and Naive Bayes | PRML: 2.1-2.2 |
ESL: 8.2 |
slides |
| September 29 | Bayesian Learning and Naive Bayes | PRML: 2.1-2.2 |
ESL: 8.3 |
slides |
| October 1 | Bayesian Learning and Continuous Decisions | PRML: 1.5, 2.3 |
slides | |
| October 6 | Generative Gaussian Classification | PRML: 2.3, 4.2 |
slides | |
| October 8 | Multivariate Gaussian Distributions | PRML: 2.3, C |
slides | |
| October 13 | Discriminative Linear Regression | PRML: 3.1-3.3 |
ESL: 3.2, 3.4 |
slides |
| October 15 | Discriminative Logistic Regression | PRML: 4.1, 4.3 |
ESL: 4.1-4.4 |
slides |
| October 20 | Discriminative Learning via Gradient Descent | PRML: 4.3, 5.2 | slides | |
| October 22 | Feature Induction and Neural Networks | PRML: 5.1-5.3, 5.5 | ESL: 11 | slides |
| October 27 | Feature Selection and L1 Regularization | ESL: 3.2-3.4 | slides | |
| October 29 | Sparsity and Kernels | PRML: 6.1-6.2 | slides, string kernels | |
| November 3 | Gaussian Process Regression and Classification | PRML: 6.4 | slides | |
| November 5 | Support Vector Machines, Decision Trees | PRML: 7.1, 14.4 | ESL: 12.2-12.3 | slides |
| November 10 | Clustering and the K-Means Algorithm | PRML: 9.1-9.2 | ESL: 13.2, 14.3 | slides |
| November 12 | Mixture Models and the EM Algorithm | PRML: 9.3-9.4 | slides | |
| November 17 | Hidden Markov Models: Viterbi Algorithm | PRML: 13.1-13.2 | slides | |
| November 19 | Hidden Markov Models: Forward-Backward and EM | PRML: 13.2 | slides | |
| November 24 | Principal Components Analysis | PRML: 12.1 | ESL: 14.5 | slides |
| November 26 | THANKSGIVING | |||
| December 1 | Factor Analysis and the EM Algorithm | PRML: 12.2 | slides | |
| December 3 | Review and Advanced Topics | PRML: 13.3 | slides | |
| December 8 | READING PERIOD (FINAL) | |||
| December 10 | READING PERIOD (FINAL) |