| Date | Content | Reading | Slides |
|---|---|---|---|
| Intro, Maximum Likelihood, Linear Regression, Overfitting, Regularization | |||
| 1/4 W | Welcome/overview Maximum likelihood for Bernoulli , HW0 out. |
Murphy 1.1 - 1.3, 2.2-2.6, 4.1, 6.4 Optional: HTF 1, Wasserman 9.1.-9.8 |
Combined week 1 Slides , |
| 1/6 F | Maximum likelihood for Gaussian, Linear least squares |
Murphy 6.4, 7.1-7.3
Optional: HTF 3.1-3.2 |
Annotated Lecture 2 MLE notes |
| 1/9 M | Linear least squares |
Murphy 6.4, 7.1-7.3
Optional: HTF 3.1-3.2 |
Week 2 slides slides annotated slides |
| 1/11 W | Linear and Polynomial regression
HWO due, HW1 out |
Murphy 6.4, 7.5.1, Optional: HTF 7.1-7.4 |
Slides annotated slides |
| 1/13 F |
Polynomial regression, Bias-variance. Overfitting
|
Murphy 6.4, 7.5.1 Optional: HTF 7.1-7.4 |
Slides (annotated) |
| 1/16 M |
MLK day, no class
|
||
| 1/18 W | Bias-Variance |
Murphy 6.4-6.5 Optional: HTF 7.1-7.4 |
Week 3
slides slides, annotated slides |
| 1/21 F | More bias-variance, regularization, ridge regression | slides,
annotated slides |
|
| 1/23 M | Ridge regression, cross-validation |
Murphy 6.4-6.5,
Optional: HTF 7.1-7.4, 3 |
Week 4 slides slides Annotated |
| Optimization | |||
| 1/25 W |
Cross-validation, Lasso HW1 due, HW2 out |
Murphy 8.1-8.3, 8.5
Optional: HTF 4.1-4.2, 4.4 |
Annotated slides |
| 1/27 F | Convexity, Gradient Descent | Annotated slides | |
| 1/30 M | Gradient Descent/SGD |
Murphy 8.1-8.3, 8.5
Roughgarden-Valiant notes Optional: Gradient descent algorithms by Ruder Optional: Zen of gradient descent by Moritz Hardt Optional: HTF 4.1-4.2, 4.4 |
Annotated slides |
| 2/1 W | Coordinate Descent and intro to Classification |
Murphy 8.1-8.3, 8.5
Optional: HTF 4.1-4.2, 4.4 |
Slides Annotated Slides |
| Classification | |||
| 2/3 F | Classification/Logistic Regression | Murphy Chapter 10 until 10.2.1, Murphy Chapter 10 until 10.2.3 | Annotated slides |
| 2/6 M |
Logistic Regression
HW2 due |
Murphy Chapter 10.3 until 10.3.2, 17.3.1, 17.3.3 | Annotated Slides |
| 2/8 W | Support Vector Machines (SVMs) + Review | Murphy Chapter 17.3.1, 17.3.3, 17.3.6 | Slides Annotated Slides |
| 2/10 F |
Midterm (in class)
HW3 out |
||
| Non-linear models | |||
| 2/13 M | Support Vector machines | Murphy Chapter 17.3.1, 17.3.3, 17.3.6 | Slides Annotated Slides |
| 2/15 W | Kernels, Non-linear models |
slides , Annotated slides |
|
| 2/17 F | Bootstrap |
Slides , Annotated Slides |
|
| 2/20 M | President's Day, no class | ||
| 2/22 W | Neural Networks | Slides | |
| 2/24 F | Backpropagation | Slides | |
| 2/27 M |
Convolutional Neural Networks
HW3 due, HW4 out |
Slides - All Annotated Slides on NNs, backprop, CNNs |
|
| Unsupervised Learning | |||
| 3/1 W | Remainder of NNs |
HTF 2.3-2.5, 7.11, 8.2 Murphy 6.1-6.3, 12.1.0, 12.2 Daume, chapter 15 Optional: HTF 5.9, 12.3; 14.5 PCA note 1 and notes 2 |
|
| 3/3 F | K means, Principal Component Analysis | Slides | |
| 3/6 M | PCA computation, SVD |
HTF 14.3.6-14.3.7 Murphy 11.2-11.4, k-means ++ paper |
Annotated Slides |
| Additional Topics | |||
| 3/8 W | Questions of fairness and equity in ML | ||
| 3/10 F |
Trees, random forests, boosting, matrix completion
HW4 due |
Murphy 16.2, 16.4 | Slides |
| 3/15 W | Final Exam |