Lectures

Note: Lecture Slides and Reading will be posted after each lecture.

Jan. 08
Lecture 1: Introduction
Reading: Wagstaff
Jan. 10
Lecture 2: Decision Trees
Reading: [TM]Ch.3, [D]Ch.1
Jan. 15
Reading: [HTF]Sec2.9, [D]Ch.2
Assignment 1 out
Jan. 17
Reading: [HTF]Sec13.3-13.3.2, [D]Ch.3, [TM]Ch.8
Jan. 22
Lecture 5: Perceptron
Reading: [D]Ch.4, [B]Sec 4.1.7
Jan. 24
Lecture 6: Linear Regression
Reading: [D]Ch.7.1-7.6, [B]Sec.3.1, 3.2
Assignment 1 due
Jan. 29
Lecture 7: Probabilistic Methods
Reading: TBD
Assignment 2 out, Proposal due
Jan. 31
Lecture 8: Linear Models for Classification
Reading: TBD
Feb. 05
Lecture 9: Support Vector Machines I
Reading: TBD
Feb. 07
Lecture 10: Support Vector Machines II
Reading: TBD
Assignment 2 due
Feb. 12
Lecture 11: Feature Construction and Selection
Reading: TBD
Feb. 14
Midterm (in class)
Feb. 19
Reading Break (no class)
Feb. 21
Reading Break (no class)
Feb. 26
Lecture 12: Ensemble Learning
Reading: TBD
Assignment 3 out
Feb. 28
Lecture 13: Active Learning
Reading: TBD
Milestone report due
Mar. 05
Lecture 14: Semi-Supervised Learning
Reading: TBD
Mar. 07
Lecture 15: Unsupervised Learning
Reading: TBD
Assignment 3 due
Mar. 12
Lecture 16: Learning with Hidden Variables
Reading: TBD
Assignment 4 out
Mar. 14
Lecture 17: Neural Networks I
Reading: TBD
Mar. 19
Lecture 18: Neural Networks II
Reading: TBD
Mar. 21
Lecture 19: Structured Prediction Models
Reading: TBD
Assignment 4 due
Mar. 26
Lecture 20: Reinforcement Learning
Reading: TBD
Assignment 5 out
Mar. 28
Lecture 21: Learning by Demonstration
Reading: TBD
Apr. 02
Lecture 22: Bias, Fairness, Intepretability
Reading: TBD
poster (PDF) due
Apr. 04
Poster Session
Assignment 5 due
Apr. 15
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Final report due