Lectures

Time and Location

Tuesday & Thursday, 2:30pm to 3:50pm ET, with class Zoom links on the Canvas page.

Lecture Recordings

Lecture recordings are available through Canvas Media Library.

Schedules


Date Topics Book Chapters Slides Notes
Thursday, Jan 21 Intro, ERM framework 1, 2.0, 2.1, 2.2
Tuesday, Jan 26 Halfspaces and Perceptron

9.0, 9.1.0, 9.1.2

Thursday, Jan 28 Linear and Polynomial Regression

9.2

Tuesday, Feb 2 Logistic Regression

9.3, 12.1.1, 14.0, 14.1.0

Thursday, Feb 4 SGD, Data Prep, and other Practicalities

14.3.0, 14.5.1

Tuesday, Feb 9 PAC Learning

2.3, 3

Thursday, Feb 11 The Bias-Complexity Tradeoff

5

Tuesday, Feb 16 LONG WEEKEND, NO CLASS

Thursday, Feb 18 Model Selection, Validation, and Regularization

11.0, 11.2, 11.3, 13.1, 13.4

Tuesday, Feb 23 Boosting

10

Thursday, Feb 25 Decision Trees

18

Tuesday, Mar 2 Learning via Uniform Convergence 4
Thursday, Mar 4 VC Dimension 6, 9.1.3
Tuesday, Mar 9 Naive Bayes 24.0, 24.1, 24.2
Thursday, Mar 11 K-Nearest Neighbors / Fairness in Machine Learning 19
Tuesday, Mar 16 Support Vector Machines 15
Thursday, Mar 18 Kernel Methods 16
Tuesday, Mar 23 Neural Networks 20.0, 20.1, 20.2, 20.3
Thursday, Mar 25 Backpropagation 20.6
Tuesday, Mar 30 K-Means 22.0, 22.2, 22.5
Thursday, Apr 1 Expectation Maximization 24.4
Tuesday, Apr 6 Principal Component Analysis 23.0, 23.1
Thursday, Apr 8 Ethics in Machine Learning
Tuesday, Apr 13 NO CLASS (READING PERIOD)
Thursday, Apr 15 NO CLASS (READING PERIOD)