Lectures

Time and Location

Tuesday & Thursday, 2:30pm to 3:50pm ET, with recordings available on the Canvas page.

Lecture Recordings

Lecture recordings are available through Canvas Media Library.

Schedules


Date Topics Book Chapters Slides Notes
Thursday, Jan 27 Intro, ERM framework 1, 2.0, 2.1, 2.2 Link
Tuesday, Feb 1 Halfspaces and Perceptron

9.0, 9.1.0, 9.1.2

Link
Thursday, Feb 3 Linear and Polynomial Regression

9.2

Link
Tuesday, Feb 8 Logistic Regression

9.3, 12.1.1, 14.0, 14.1.0

Link
Thursday, Feb 10 SGD, Data Prep, and other Practicalities

14.3.0, 14.5.1

Link
Tuesday, Feb 15 PAC Learning

2.3, 3

Link
Thursday, Feb 17 The Bias-Complexity Tradeoff

5

Link
Tuesday, Feb 22 LONG WEEKEND, NO CLASS

Thursday, Feb 24 Model Selection, Validation, and Regularization

11.0, 11.2, 11.3, 13.1, 13.4

Link
Tuesday, Mar 1 Boosting

10

Link
Thursday, Mar 3 Decision Trees

18

Link
Tuesday, Mar 8 Learning via Uniform Convergence 4 Link
Thursday, Mar 10 VC Dimension 6, 9.1.3 Link
Tuesday, Mar 15 Naive Bayes 24.0, 24.1, 24.2 Link
Thursday, Mar 17 K-Nearest Neighbors / Fairness in Machine Learning 19 Link
Tuesday, Mar 22 Support Vector Machines 15 Link
Thursday, Mar 24 Kernel Methods 16 Link
Tuesday, Mar 29 SPRING BREAK, NO CLASS
Thursday, Mar 31 SPRING BREAK, NO CLASS
Tuesday, Apr 5 Neural Networks 20.0, 20.1, 20.2, 20.3 Link
Thursday, Apr 7 Backpropagation 20.6 Link
Tuesday, Apr 12 Deep Learning 20.6 Link Lecture Notebook
Question Notebook
Thursday, Apr 14 K-Means 22.0, 22.2, 22.5 Link
Tuesday, Apr 19 Expectation Maximization 24.4 Link
Thursday, Apr 21 Principal Component Analysis 23.0, 23.1 Link
Tuesday, Apr 26 Ethics in Machine Learning Link
Thursday, Apr 28 Review / Cutting Edge Machine Learning Link
Tuesday, May 3 NO CLASS (READING PERIOD)
Thursday, May 5 NO CLASS (READING PERIOD)
Tuesday, May 10 NO CLASS (READING PERIOD)