🤖 Statistical Learning

📈 Course Metadata

• Institution: School of Mathematical Sciences
• Prerequisites: Mathematical Analysis, Advanced Algebra, Probability Theory, Mathematical Statistics, Regression Analysis
• Course Type: Core undergraduate course for Statistics majors

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📚 References & Credits

This set of notes is built upon the following resources:

1. Course PPT: Special thanks to the instructor for providing detailed lecture notes. The content structure and derivations in this MkDocs note deeply reference the instructor's PPT organization.
2. The Elements of Statistical Learning

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🗺️ Syllabus & Navigation

Below is the detailed table of contents for these notes:

Part I: Basic Supervised Learning Methods

• Chapter 1: KNN
• Chapter 2: Linear Regression

Part II: Regularization and High-Dimensional Linear Models

• Chapter 3: Penalized Linear Regression (I)
• Chapter 4: Penalized Linear Regression (II)

Part III: Classification and Maximum Margin Methods

• Chapter 5: Classification
• Chapter 6: Support Vector Machines

Part IV: Kernel Methods and Nonparametric Estimation

• Chapter 7: RKHS and Kernel Regression
• Chapter 8: Kernel Density Estimation

Part V: Tree Models and Ensemble Learning

• Chapter 9: Tree Models

Part VI: Causal Inference

• Chapter 10: Causal Inference

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📝 Notes on the Notes

Construction Complete

The content of this page is essentially complete. Long proofs and exercise solutions in the notes adopt a collapsible block design. If you find any typos, feel free to submit an Issue or contact me directly!

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💬 Comments