| Date | Topic | Readings, assignments | Scribe notes |
| Jan 7 | Introduction to Machine Learning | HW0 | |
| Jan 9 | Linear regression | HW1 due Jan 18, Notes from Andrew Ng | |
| Jan 14 | Logistic regression | Notes (Ng) and Chap 4.3 from Bishop, Log. Reg. tutorial | |
| Jan 16 | Generalized linear models | Notes (Ng) | |
| Jan 23 | Generative vs discriminative | HW2 due Feb 4, Chapter 4.2 from Bishop | |
| Jan 28 | Maximum likelihood estimation | ||
| Jan 30 | Loss functions | Read Chapter 5.1-5.3 of Bishop; introduction to Neural Networks. | |
| Feb 4 | Neural Networks | Notes (Bishop) | Lecture8 |
| Feb 6 | Support Vector Machines | Notes (Ng), HW3 due Feb 20 | Lecture9 |
| Feb 11 | Kernal methods | SVM tutorial | Lecture10 |
| Feb 13 | Decision trees | Lecture11 | |
| Feb 20 | Learning Theory | Notes (Ng) | Lecture12 |
| Feb 25 | Bias-variance | HW4 due Mar 5 | Lecture13 |
| Feb 27 | Regularization and PCA | Writeup from David McAllester, and writeup from Ng | Lecture14 |
| Mar 3 | Linear dimensionality reduction cont'd (FDA,CCA) | Slides from Michael Jordan's class, Project proposals due | |
| Mar 5 | Boosting & other ensemble methods | Bishop 3.3-3.4, Boosting tutorial | |
| Mar 10 | Unsupervised learning: kmeans, mixture models, and EM | EM tutorial | |
| Mar 12 | |||
| Mar 21 | Project Presentations 1-4pm DBH 3011 |