MSSC 6250 - Statistical Machine Learning

This page contains an outline of the topics, content, and assignments for the semester. Note that this schedule will be updated as the semester progresses, with all changes documented here.

Week	Date	Topic	To Do	Slides	In-class Activities	Homework	Project
1	Tue, Jan 16	Syllabus/Overview of Statistical Learning	📖	🖥️🖥️
	Thu, Jan 18	Bias-variance tradeoff		🖥️
2	Tue, Jan 23	Linear Regression (review)	📖	🖥️		✍️
	Thu, Jan 25	Linear Regression (optimization)
3	Tue, Jan 30	Ridge Regression	📖	🖥️
	Thu, Feb 1	Cross-Validation
4	Tue, Feb 6	Feature Selection	📖	🖥️
	Thu, Feb 8	LASSO				✍️
5	Tue, Feb 13	Splines	📖	🖥️
	Thu, Feb 15	In-class Activity: Bootstrapping			✅
6	Tue, Feb 20	Bayesian Inference	📖	🖥️
	Thu, Feb 22	Bayesian Linear Regression
7	Tue, Feb 27	Binary Logistic Regression	📖	🖥️
	Thu, Feb 29	Binary Logistic Regression
8	Tue, Mar 5	Classification Performance Measures	📖
	Thu, Mar 7	Multinomial Logistic Regression; In-class Activity: Generalized Linear Model and Generalized Additive Model			✅	✍️
9	Tue, Mar 12	NO CLASS: Spring break
	Thu, Mar 14	NO CLASS: Spring break
10	Tue, Mar 19	Discriminant Analysis	📖	🖥️
	Thu, Mar 21	Naive Bayes
11	Tue, Mar 26	K-Nearest Neighbors Regression	📖	🖥️
	Thu, Mar 28	NO CLASS: Easter break
12	Tue, Apr 2	K-Nearest Neighbors Classification
	Thu, Apr 4	Gaussian Process Regression
13	Tue, Apr 9	Support Vector Machine	📖	🖥️
	Thu, Apr 11	Support Vector Machine; In-class activity: Loss functions			✅	✍️
14	Tue, Apr 16	CART and Bagging	📖	🖥️
	Thu, Apr 18	Random Forests and Boosting
15	Tue, Apr 23	Dimension Reduction	📖	🖥️
	Thu, Apr 25	Principal Component Analysis					📂
16	Tue, Apr 30	K-Means Clustering	📖	🖥️
	Thu, May 2	Model-based Clustering; In-class activity: Principal Component Regression and Partial Least Squares			✅

I reserve the right to make changes to the schedule.