Lectures

Tuesday & Thursday, 2:30pm-3:50pm, MacMillan 117. Lecture schedule is subject to change.

Lecture recordings are available through Canvas Media Library or here.


Date Topics Supplementary Readings Primary Materials
January 25 Introduction slides
January 30 Decision Stumps

February 1 Probably Approximately Correct

Ch 2

February 6 Advanced Numpy

notebook
February 8 Decision Trees

Ch 18

slides
February 13 Means and Medians

February 15 Naive Bayes

Ch 24.1, Ch 24.2

February 20 No Class

February 22 Logistic Regression

Ch 9.3, Ch 12.1

slides
February 27 K-Nearest Neighbors

Ch 19

March 1 K-Means

Ch 22.2

March 6 VC Dimension Ch 6 slides
March 8 Perceptrons Ch 9.1
March 13 No Class
March 15 Support Vector Machines Ch 15
March 20 Midterm Sample Midterm, Solutions
March 22 Kernel Methods Ch 16 slides
April 3 Linear Regression Ch 9.2
April 5 Principal Component Analysis Ch 23.1
April 10 Feedforward Neural Nets Ch 20.1
April 12 Boosting Ch 10 slides
April 17 Backpropagation Ch 20.6
April 19 Random Projections Ch 23.2 slides
April 24 The EM Algorithm Ch 24.4
April 26 Summary/Overview
May 1 Michael: AMA