|
| Lecture | Date | Topic | Textbook Reading | Background Reading | Homework Assigned | Homework Due | |
|---|---|---|---|---|---|---|---|
| Lecture 1 | Wed Sept 7th | Overview of Machine Learning | Sec 1.1 (B), Chapter 2 (HTF). | Read this article on a computational theory of learning, due to Les Valiant, winner of the 2011 Turing Award. | |||
| Lecture 2 | Mon Sept 12th | Statistical Models | Sec 1.2, Sec 2.1-2.3 (B), Sec. 2.1-2.4, 23.1-23.3 (M). | Read this article on the $1 million Netflix datamining contest | |||
| Lecture 3 | Wed Sept 14th | Regression, Regularization and Model Selection | Sec 1.1, 1.3, 3.1 (B), Sec 3.1-3.4 (HTF). | Read this article on least angle regression (Sections 1-3). | Homework 1 | ||
| Lecture 4 | Mon Sept 19th | Linear classification; Bayesian Decision Theory | Sec 1.5, Sec 4.1-4.2 (B), Ch. 2.1-2.6 (DHS). | Read this article on self taught learning . | |||
| Lecture 5 | Wed Sept 21st | Causal and Graphical Models | Chapter 8 (B) . | Read this review article on causal modeling . | |||
| Lecture 6 | Mon Sept 26th | Principles of Statistical Data Reduction | Chapter 6 (CB). | Read this review article on conditional independence in statistics . | HW 1 | ||
| Lecture 7 | Wed Sep 28th | Maximum Likelihood and Bayesian Estimation | Sec. 2.3-2.4 (B), Ch. 2, 5 (W), Ch. 22 (M). | Read this review article on the optimality of the naive Bayes classifier . | Homework 2 | ||
| Lecture 8 | Mon Oct 3th | Bias Variance Analysis; Cramer-Rao Theorem | Sec. 3.2 (B), Ch. 3 (W). | Read this article on probabilistic canonical correlational analysis . | |||
| Lecture 9 | Wed Oct 5th | Expectation Maximization Algorithm; Mixture Models | Ch. 9 (B), Ch. 20 (M). | Read this article on scientific topic modeling . | |||
| Lecture 10 | Tue Oct 11th (Mon schedule!) | Project Proposals | Read this article on 3D Map Learning on mobile robots using EM . | HW 2 | |||
| Lecture 11 | Wed Oct 12th | Time-series data and Hidden Markov Models | Ch. 13 (B). | Read this article on a variational algorithm for learning abstract hidden Markov models . | Homework 3 | ||
| Lecture 12 | Mon Oct 17th | Learning to Control Dynamical Systems | Read this Survey article . | ||||
| Lecture 13 | Wed Oct 19th | Logistic Regression; Generalized Linear Models | Sec. 4.3 (B), Sec. 4.4 (HTF) | HW 3 | Lecture 14 | Mon Oct 24th | Maximum Entropy Framework | Midterm Exam | Sample midterm exam |
| Lecture 16 | Wed Oct. 26th | Instance-based Learning; Kernel Methods | Ch. 6 (B) | Read this article on maximum entropy methods . | |||
| Lecture 17 | Mon Oct. 31st | Support Vector Machines | Ch. 7 (B), Ch. 12 (HTF) | Read this article on string kernels . | |||
| Lecture 18 | Wed Nov 2nd | Reproducing Kernel Hilbert Spaces | Homework 4 | Lecture 19 | Mon Nov 7th | Computational Learning Theory | Ch. 7 (M) | Read this classic article by Gold on language identification in the limit , and Valiant's A Theory of the Learnable . |
| Lecture 20 | Mon Nov 9th | Dimensionality Reduction | Ch 12 (B), Sec. 14.4-14.7 (HTF), Section 3.8 (DHS) | ||||
| Lecture 21 | Mon Nov 14th | Manifold Learning | Read this overview of spectral methods for dimensionality reduction | ||||
| Lecture 22 | Mon Nov 16th | Learning Representation and Control in MDPs | Read this article | HW 4 | |||
| Lecture 23 | Mon Nov 21st | Project Status Report | |||||
| Lecture 24 | Mon Nov 28th | Future Directions in ML | |||||
| Wed Nov. 30th | Future Directions in ML | ||||||
| Mon Dec 5th | Project Presentations | ||||||
| Wed Dec 7th | Project Presentations | ||||||
| Fri Dec 9th | Final Project Reports and Term Papers due |