Course Information

Course Policy

Syllabus

Testing Datasets

Useful Links

Date	Topic	Reading	Assignment
R 08/24	Introduction
T 08/29	Supervised Learning - Baysian Decision Theory Last Day to Drop without "W"	Ch1.2,1.5,2.3	Project 1 - Supervised Learning (Due 09/19) HW1 (Due 09/07)
R 08/31	Supervised Learning - Discriminant Functions	Ch4.1
T 09/05	Supervised Learning - Parametric Estimation	Ch2.3,2.5
R 09/07	Supervised Learning - Non-parametric Learning	Ch9.1
T 09/12	Supervised Learning - Non-parametric Learning		HW2 (Due 09/21)
R 09/14	Dimensionality Reduction	Ch4.1.4, 4.1.6, 12.1
T 09/19	Dimensionality Reduction
R 09/21	Dimensionality Reduction
T 09/26	Unsupervised Learning	Ch9.1	Project 2 - Dimensionality Reduction and Unsupervised Learning (Due 10/17) HW3 (Due 10/05)
R 09/28	Unsupervised Learning Gradient Descent
T 10/03	Classifier Fusion
R 10/05	Classifier Fusion	Ch14.3
T 10/10	Fall Break
R 10/12	Test 1
T 10/17	Performance Evaluation		HW4 (Due 10/26)
R 10/19	Regression	Ch3.1,4.3.2,5.2.4
T 10/24	Canceled
R 10/26	Regression		Project 3 - Regression (Due 11/09)
T 10/31	Perceptron	Ch4.1.7	HW5 (Due 11/07)
R 11/02	Assignment Discussion Neural Network and Back Propagation
T 11/07	Neural Network and Back Propagation	Ch5.1-5.3, [Nielsen]Ch1-4
R 11/09	Neural Network and Back Propagation Test Discussion and Review
T 11/14	Review Last Day for Graduate Students to Drop with “W”
R 11/16	Test 2		Project 4 - Neural Network (Due 11/30)
T 11/21	Support Vector Machine		Final Project Milestone 1: Group Formation and Topic Selection (Due 11/28)
R 11/23	Thanksgiving
T 11/28	Decision Tree and Random Forest		Milestone 2: Introduction and Literature Survey (Due 12/05)
R 11/30	Decision Tree and Random Forest From ML to DL		Milestone 3: Prototype 1 Construction (Due 12/07)
T 12/05	From ML to DL
W 12/13	Final Presentation (3:30-6:00pm)