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

February 6 Advanced Numpy

notebook
February 8 Decision Trees

slides
February 13 Means and Medians

February 15 Naive Bayes and Logistic Regression

slides
February 20 No Class

February 22 K-Nearest Neighbors

February 27 K-Means

March 1 VC Dimension

March 6 Perceptrons
March 8 Support Vector Machines
March 13 Slack
March 15 Kernel Methods
March 20 Midterm
March 22 Boosting
April 3 Linear Regression
April 5 Principal Component Analysis
April 10 Feedforward Neural Networks
April 12 Slack
April 17 Random Projections
April 19 Hidden Markov Models
April 24 Recurrent Neural Networks
April 26 Contextual Bandits
May 1 Slack
May 3 Slack
May 8 Slack