Statistical Learning Notes Outline

This note outlies topics about statistical learning that I want to update. Each note serves as a summary of my learnings, including concepts, R/Python methods, and use cases. Though ISLR2 will be the main reference, readings and learnings from other resources (such as blog posts or papers) will be cited when necessary.

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Topics

1. Fundamentals

2. Linear Regression

3. Lasso, Ridge, and ElasticNet

4. Logistic Regression

5. Tree-based models (Decision Trees, Random Forests, Bagging, Boosting)

6. XGBoost

7. Regression and Classification Metrics

8. Support Vector Machine

9. Unsupervised Learning

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