Overview
Core references: MIT 6.867 Lecture Notes.
This page will hold PDFs for lecture notes and short quizzes. As we progress, links will light up; until then you’ll see a friendly TBA.
Lecture Schedule & Uploads
| # | Topic | Materials (upload here) |
|---|---|---|
| Lecture 01 | Introduction, linear classification, perceptron update rule | |
| Lecture 02 | Perceptron convergence, generalization | |
| Lecture 03 | Maximum margin classification | |
| Lecture 04 | Classification errors, regularization, logistic regression | |
| Lecture 05 | Linear regression, estimator bias and variance, active learning | |
| Lecture 06 | Active learning (cont.), non-linear predictions, kernels | |
| Lecture 07 | Kernel regression, kernels | |
| Lecture 08 | Support vector machines (SVM) & kernels; kernel optimization | |
| Lecture 09 | Model selection | |
| Lecture 10 | Model selection criteria | |
| Lecture 11 | Description length, feature selection | |
| Lecture 12 | Combining classifiers, boosting | |
| Lecture 13 | Boosting, margin, and complexity | |
| Lecture 14 | Margin and generalization, mixture models | |
| Lecture 15 | Mixtures and the expectation–maximization (EM) algorithm | |
| Lecture 16 | EM, regularization, clustering | |
| Lecture 17 | Clustering | |
| Lecture 18 | Spectral clustering, Markov models | |
| Lecture 19 | Hidden Markov models (HMMs) | |
| Lecture 20 | HMMs (cont.) | |
| Lecture 21 | Bayesian networks | |
| Lecture 22 | Learning Bayesian networks | |
| Lecture 23 | Probabilistic inference — guest lecture on collaborative filtering | |
| Lecture 24 | Current problems in machine learning, wrap up |