STAT 432: Basics of Statistical Learning

Welcome!

This the official website of Basics of Statistical Learning (Stat 432). To get started, please read the Course Syllabus.

Our class meets Tue and Thur at 160 English Building. There are two sessions: 11:00AM - 12:20PM and 12:30PM - 1:50PM.
In each week, we will focus on a particular topic. Each topic is associated with a list of objectives which you need to comprehend and master. Besides meeting in class, class notes and R examples are available. We will try to practice some of them in class.
Homework assignments are designed around these objectives. They will be posted at the beginning of each week and due at Thursday 11:59PM CT of the following week. Late submission is allowed, but with penalty. Please see the syllabus for more details.
All homework should be submitted (.pdf file) to gradescope. Please use entry code: NYG75N to enroll.
Course discussion forum and grades are available at Canvas.

In person:
- Mon 4pm, CAB 137;
- Tue 3-5PM: 317 Gregory Hall (all dates except that we use 1032 LCLB on Sep 17)
Zoom:
- Wed 11AM, Thur, 10-11AM, 5-6PM
- [Zoom Link, pw:432]

Week	Topic
Week 1 (Aug 26)	Introduction
Week 2 (Sep 2)	Linear Regression and Bias-variance Trade-off
Week 3 (Sep 9)	Optimization and Ridge Regression (recorded video)
Week 4 (Sep 16)	Lasso
Week 5 (Sep 23)	K-nearest neighbors and nonparametric estimation
Week 6 (Sep 30)	Kernel Methods, Smoothing and Adaptiveness
Week 7 (Oct 7)	Support Vector Machines
Week 8 (Oct 14)	Classification and Discriminate Analysis
Week 9 (Oct 21)	Ensemble Models: Random Forests and Boosting
Week 10 (Oct 28)	Unsupervised Learning Algorithms
Week 11 (Nov 4)	RKHS and Related Models
Week 12 (Nov 11)	Exam, No Class on Nov 14
Week 13 (Nov 18)	Causal Inference and Personalized Medicine
Week 14 (Nov 25)	Fall Break
Week 15 (Dec 2)	Final Project Presentation
Week 16 (Dec 9)	Final Project Presentation