Lectures

Time & Location

Tuesday & Thursday, 2:30 pm - 3:50 pm, at Metcalf Research Building Auditorium.

Lecture recordings

Lecture recordings are available through Canvas Media Library or here.

Schedules


Date Topics Book Chapters Slides Notes
January 24 Introduction, ERM framework Ch 1, Ch 2.0, Ch 2.1, Ch 2.2 slides
January 29 Halfspaces and Perceptron

Ch 9.0, Ch 9.1.0, Ch 9.1.2

slides
January 31 Linear and Polynomial Regression

Ch 9.2

slides
February 5 Logistic Regression and Gradient Descent

Ch 9.3, Ch 12.1.1, Ch 14.0, 14.1.0, 14.3.0, 14.5.1

slides
February 7 PAC Learning

Ch 2.3, Ch 3

slides
February 12 Learning via Uniform Convergence

Ch 4

slides
February 14 Learning under Uncertainty

Ch 24.0, Ch 24.1

slides
February 19 No Class, Long Weekend

February 21 Naive Bayes

Ch 24.2

slides
February 26 The Bias-Complexity Tradeoff

Ch 5

slides
February 28 Model Selection, Validation, and Regularization

Ch 11.0, Ch 11.2, Ch 11.3, Ch 13.0, Ch 13.1, Ch 13.4

slides
March 5 VC Dimension Ch 6.0, Ch 6.1, Ch 6.2, Ch 6.3, Ch 6.4, Ch 9.1.3 slides
March 7 Boosting Ch 10 slides
March 12 Support Vector Machines Ch 15 slides
March 14 Kernel Methods Ch 16 slides
March 19 Decision Trees Ch 18 slides
March 21 Midterm
March 26 No Class, Spring Break
March 28 No Class, Spring Break
April 2 K-Nearest Neighbors Ch 19
April 4 Neural Networks Ch 20.0, Ch 20.1, Ch 20.2, Ch 20.3
April 9 Backpropagation Ch 20.6
April 11 K-Means Ch 22.0, Ch 22.2, Ch 22.5
April 16 Expectation Maximization Ch 24.4
April 18 Principal Component Analysis Ch 23.0, Ch 23.1
April 23 Ethics in Machine Learning
April 25 Summary/Overview
April 30 Reading Period
May 2 Reading Period
May 7 Reading Period
May 14 Final Exam