Overview
Explore predictive modeling techniques such as decision trees and Support Vector Machines. These non-parametric methods adapt to the underlying data structure without assuming a fixed functional form.
Topics Covered
- Decision Trees and Random Forests
- Boosting and Gradient Boosted Trees
Resources
Key Topics, Meeting Notes / Lesson Objectives, and Assignments
| Date | Key Topics | Meeting Notes and Lesson Objectives | Assignments |
|---|---|---|---|
| 28 Jan / 17 Mar 2026 | Lesson 10: Non-Parametric Models 1 | Meeting notes linked here. (1) Understand Decision Trees for regressin and classification, (2) Bagging and (3) the difference between Parametric and Non-Parametric Models. | Geron pp. 179-189 (pdf), ISLP 331-346 Complete Coursera course. |
| 19 Feb 2026 / 24 Mar 2026 | Lesson 11: Support Vector Machines (SVMs) | Meeting notes linked here. (1) Understand SVMs | Geron pp. 159-176 (pdf), ISLP 367-382 |
| 31 Mar 2026 | Lesson 11a: SVM and KKT Conditions | Meeting notes linked here. (1) Apply use of KKT Conditions | PMLR 326-345 |