Content and tentative timeline
| Date | Lecture | Notes |
|---|---|---|
| Week 1 Oct 4 |
 Introduction; Supervised Learning Setup; kNNs |
|
| Week 2 Oct 11 |
No lecture |
|
| Week 3 Oct 18 |
Linear models for regression |
Project 1 |
| Week 4 Oct 25 |
Linear Models for Classification; The Perceptron; Gradient Descent |
|
| Week 5 Nov 8 |
Linear Classification |
|
| Week 6 Nov 15 |
 Lab 1 |
Project 1 due |
| Week 7 Nov 22 |
Lab 2 - Lecture: LDA & Logistic Regression |
Project 2 |
| Week 9 Nov 29 |
Support Vector Machines and Kernels |
|
| Week 10 Dec 6 |
Bias Variance Decomposition; Regularization |
|
| Week 11 Dec 13 |
Lab 3 - Lecture: Validation recap and Kernels |
Project 3 |
| Week 12 Dec 20 |
Trees |
Project 2 due |
| Week 13 Jan 10 |
|
|
| Week 14 Jan 17 |
|
|
| Week 15 Jan 23 |
|
|
| Week 15 Jan 24 |
Lab 4 |
Project 3 due |