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
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
- Topic 06: Generalized Linear Models
- Topic 07: Non-linear Models
- Topic 08: Tree-based Methods
- Topic 09: Boosting Methods
- Topic 10: Support Vector Machines
Class Presentation (Project I)
- Please click here to download guidelines, group-wise topics, and presentation schedule.
- Please click here to download beamer template for preparing your presentation.