229351 | Statistical Learning for Data Science 1
Winter 2022
Time: M & Th 11.00am-13.00am at SCB4202
Instructor
- Donlapark Ponnoprat
Office: STB304
Grading
- Homework 30%
- Midterm 35%
- Final 35%
Additional readings
- [ISLR] Trevor Hastie, Robert Tibshirani, Daniela Witten and Gareth James, An Introduction to Statistical Learning: with Applications in R [book] [python code]
- [FPP] Rob J Hyndman and George Athanasopoulos, Forecasting: Principles and Practice [online book]
- [LAML] Charu C Aggarwal, Linear Algebra and Optimization for Machine Learning
Lecture Notes
| Date | Topic | Notes/HW | Labs | Readings |
|---|---|---|---|---|
| Nov 16 | Introduction | Lecture 1 | Lab 1 | ISLR 2.1.5 |
| Nov 23 | Linear algebra |
Lecture 2 |
Lab 2 | LAML 1.2.1-1.2.4, 2.3.1-2.3.2 |
| Nov 20 | Principal component analysis (PCA) | Lecture 3 |
ISLR 10.2.1-10.2.2 LAML 3.3, 3.3.7 |
|
| Nov 27 | Linear regression I | Lecture 4 | ISLR 3.1 | |
| Dec 4 | No Class | Lab 3-1 Lab 3-2 | ISLR 3.1 | |
| Dec 11 | No Class | Lab 4 | ISLR 3.1 | |
| Dec 18 | Linear regression II |
Lecture 5 |
Lab 5 | ISLR 3.2 |
| Dec 25 | Linear regression III | Lecture 6 | Lab 6 | ISLR 3.3 |
| Jan 25 | Midterm week | |||
| Jan 22 | Time series I | Lecture 7 | Lab 7 | FPP 2.6-2.8, 8.1 |
| Feb 5 | Time series II | Lecture 8 | Lab 8-1 Lab 8-2 | FPP 6.1-6.3, 6.7, 6.8 |
| Feb 12 | Time series III | Lecture 9 | Lab 9-1 Lab 9-2 | FPP 7.1-7.4, 7.6 |
| Feb 19 | Logistic regression I | Lecture 10 | Lab 10 | ISLR 4.2, 4.3.1-4.3.3 |
| Feb 29 | Model evaluation | Lecture 11 | ISLR 4.3.4-4.3.5, |