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 slides
Tuesday, Jan 26 Halfspaces and Perceptron

9.0, 9.1.0, 9.1.2

slides
Thursday, Jan 28 Linear and Polynomial Regression

9.2

slides
Tuesday, Feb 2 Logistic Regression

9.3, 12.1.1, 14.0, 14.1.0

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

14.3.0, 14.5.1

slides
Tuesday, Feb 9 PAC Learning

2.3, 3

slides
Thursday, Feb 11 The Bias-Complexity Tradeoff

5

slides
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

slides
Tuesday, Feb 23 Boosting

10

slides
Thursday, Feb 25 Decision Trees

18

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