| Date |
Key Topics |
Meeting Notes and Lesson Objectives |
Assignments |
| 13 Jan 2026 |
Lesson 01: Introduction to Statistical Learning 1
|
Meeting notes linked
here.
Discuss basics of statistical learning, focusing on references,
tools, vocabulary, model error, and the objectives of the course.
|
ISLP 1-39; MLP Mod 1
|
| 20 Jan 2026 |
Lesson 02: Introduction to Statistical Learning 2
|
Meeting notes linked
here.
Introduce Google
Colab,
the general data-science process, and exploratory data analysis.
|
Read the short article on EDA
here.
|
| 27 Jan 2026 |
Lesson 03: Exploratory Data Analysis
|
Colab notebook with EDA examples
here.
Review exploratory analysis, data visualization, and the use of
Overleaf
for mathematical and technical writing.
|
Review and select a dataset for modeling demonstrations.
|
| 03 Feb 2026 |
Lesson 04: Resampling, Cross-Validation & Bootstrapping
|
Meeting notes linked
here.
Colab CV, resampling, and bootstrap examples
here.
Understand the end-to-end ML process, error metrics,
resampling, cross-validation, and bootstrapping.
|
Géron pp. 41-86; ISLP 201-215.
|