Lectures
Tuesday & Thursday, 2:30pm-3:50pm, MacMillan 117. Lecture schedule is subject to change.
Lecture recordings are available through Canvas Media Library or here.
| Date | Topics | Supplementary Readings | Primary Materials |
|---|---|---|---|
| January 25 | Introduction | slides | |
| January 30 | Decision Stumps | ||
| February 1 | Probably Approximately Correct |
Ch 2 |
|
| February 6 | Advanced Numpy | notebook | |
| February 8 | Decision Trees |
Ch 18 |
slides |
| February 13 | Means and Medians | ||
| February 15 | Naive Bayes |
Ch 24.1, Ch 24.2 |
|
| February 20 | No Class | ||
| February 22 | Logistic Regression |
Ch 9.3, Ch 12.1 |
slides |
| February 27 | K-Nearest Neighbors |
Ch 19 |
|
| March 1 | K-Means |
Ch 22.2 |
|
| March 6 | VC Dimension | Ch 6 | slides |
| March 8 | Perceptrons | Ch 9.1 | |
| March 13 | No Class | ||
| March 15 | Support Vector Machines | Ch 15 | |
| March 20 | Midterm | Sample Midterm, Solutions | |
| March 22 | Kernel Methods | Ch 16 | slides |
| April 3 | Linear Regression | Ch 9.2 | |
| April 5 | Principal Component Analysis | Ch 23.1 | |
| April 10 | Feedforward Neural Nets | Ch 20.1 | |
| April 12 | Boosting | Ch 10 | slides |
| April 17 | Backpropagation | Ch 20.6 | |
| April 19 | Random Projections | Ch 23.2 | slides |
| April 24 | The EM Algorithm | Ch 24.4 | |
| April 26 | Summary/Overview | ||
| May 1 | Michael: AMA |