MA 212M: Understanding Statistical Learning Theory

Course Syllabus

• Linear Regression
• Linear Model Selection and Regularization
• Classification
• Non-linear Regression
• Tree-based Methods
• Support Vector Machine
• Deep Learning
• Multiple Testing
• Survival Analysis

Books

Text Books

• An Introduction to Statistical Learning with Application in R by G. James, D. Witten, T. Hastie, R. Tibshirani Softcopy
• An Introduction to Statistical Learning with Application in Python by G.James, D. Witten, T. Hastie, R. Tibshirani, J. Taylor Softcopy

Reference Book

• The Elements of Statistical Learning} by T. Hastie, R. Tibshirani, J. Friedman Softcopy

Evaluation

The breakdown of the evaluation components and their respective weights is as follows:

• Project I: 20% (Selected topics will be assigned for self-study throughout the semester. Students are expected to independently study and present these topics as part of a continuous evaluation process.)
• Project II: 30% (Students are expected to complete a comprehensive data analysis project and present their findings. Presentations will be scheduled toward the end of the semester. Further details will be provided in class.)
• Mid-semester examination: 20% (September 19 from 9 am to 11 am)
• End-semester examination: 30% (November 20 from 9 am to 12 am)

Course Materials

To download lecture slides on statistical inference and linear regression, please click here

Lecture Slides

• Topic 00: Introduction
• Topic 01: Linear Regression (A Review)
• Topic 02: Statistical Learning (An Overview)
• Topic 03: Model Selection in Linear Regression
• Topic 04: Bootstrap
• Topic 05: Classification