Learning Outcomes
- Understand how to fit and interpret linear regression models
- Understand logistic regression and probability-based classification
- Evaluate model performance using metrics like R², MSE, and classification accuracy
- Apply regression techniques to real datasets using Python
Helpful Videos
Linear Regression
Logistic Regression
Linear Algebra and Machine Learning
Bias and Variance Tradeoff
Functions in Python
Classes in Python
Textbook References
- Statistical Learning with Python (ISLP), Chapter 2.1-2.2: Statistical Learning and Model Accuracy
- Statistical Learning with Python (ISLP), Chapter 3.1-3.5: Linear Regression
- Statistical Learning with Python (ISLP), Chapter 4.1-4.3: Logistic Regression
Key Topics, Meeting Notes / Lesson Objectives, and Assignments
| Date | Key Topics | Meeting Notes and Lesson Objectives | Assignments |
|---|---|---|---|
| 10 Feb 2026 | Lesson 05: Supervised Learning 1 | Meeting notes linked here. Project 1 due 10 FEB 26 NLT 2359. Learn to (1) Apply linear regression implmentation. (2) Apply to data set and use cross-validation. (3) Understand 3 perspectives for normal equations (4) Derive normal equations from linear algebra perspective. | ISLP 69-108; MLP Mod 2. Submit Project 1 via Cavas in Latex PDF. |
| 17 Feb 2026 | Lesson 06: Supervised Learning 2 | Meeting notes linked here (1) Understand logistic regression (2) Understand lindear discriminant analysis. | ISLP 135-157; MLP Mod 3 |
| 24 Feb 2026 | Lesson 07: Project 2 Practical Supervised Model 1 | Meeting notes linked here Project 2 Classification due 17 MAR 26 NLT 2359 linked Rubric here and Notebook linked here (1) Understand shrinkage and regularization | ISLP 229-253; Github Introduction |
| 3 Mar 2026 | Lesson 08: Project 2 Practical Supervised Model 2 | Meeting notes linked here With selected dataset, preform practical linear or logistic regression. Complete full demo in Colab and write up in Overleaf. (1) Undestand classification metrics. (2) Understand gradient descent | Geron 89-111 |
| 10 Mar 2026 | Lesson 09: Project 2 Practical Supervised Model 3 | Meeting notes linked here (1) Understand the parameters and their applicaiton assocaited with several classificaiton methods. (2) Understand LDA (3) Undestand subgradient methods (4) Apply use of Github | ISLP pp. 173-188 |