CS142 | Lectures

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

Lecture recordings are available through Canvas Media Library or here.

Date	Topics	Book Chapters	Slides
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