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.

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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