CS229T - Statistical Learning Theory

This repository contains Tengyu Ma's Lecture Notes in Statistical Learning Theory (CS229T) from Stanford University, Fall 2018.

Overview

This is a comprehensive collection of lecture notes from CS229T (Statistical Learning Theory) taught by Professor Tengyu Ma at Stanford University during Fall 2018. The course covers fundamental concepts in statistical learning theory, including generalization bounds, empirical risk minimization, and theoretical foundations of machine learning.

Repository Structure

cs229t/
├── cs229t_fall_2018/          # Main course materials
│   ├── pdfs/                  # Compiled PDF versions of lecture notes
│   ├── tmu/                   # .tmu files (Typed Math Utility format)
│   └── README.md              # Course-specific documentation
├── lectures/                  # Additional lecture materials and tools
│   ├── notes.otl             # Outline format lecture notes
│   ├── notes.pdf             # Compiled PDF version
│   ├── otl2tex               # Outline to LaTeX converter
│   ├── otl2pdf               # Outline to PDF converter
│   ├── Makefile              # Build automation
│   └── utils.js              # JavaScript utilities
└── README.md                 # This file

Content

Lecture Notes (Fall 2018)

The course covers the following topics across multiple lectures:

• 09/24: Introduction to Statistical Learning Theory
• 09/26: Concentration Inequalities
• 10/01: VC Dimension and Sauer's Lemma
• 10/03: Rademacher Complexity
• 10/08: Generalization Bounds
• 10/10: Empirical Risk Minimization
• 10/15: Regularization and Model Selection
• 10/17: Online Learning
• 10/22: Multi-Armed Bandits
• 10/29: Reinforcement Learning
• 10/31: Deep Learning Theory
• 11/05: Optimization in Machine Learning
• 11/07: Stochastic Optimization
• 11/12: Convex Optimization
• 11/14: Non-convex Optimization
• 11/26: Adversarial Learning
• 11/28: Privacy in Machine Learning
• 12/03: Fairness in Machine Learning
• 12/05: Final Review

File Formats

.tmu Files (Primary Format) - Mogan & Liii STEM Exclusive

• Location: cs229t_fall_2018/tmu/
• Description: Typed Math Utility format files containing the lecture notes
• Exclusive Support: .tmu is the exclusive format for Mogan and Liii STEM
• Features:
  • Native support for mathematical formulas and scientific notation
  • Seamless LaTeX compatibility
  • AI-powered editing assistance
  • Magic paste functionality for easy content import
  • Multi-format export capabilities

PDF Files

• Location: cs229t_fall_2018/pdfs/
• Description: Compiled PDF versions of all lecture notes
• Usage: Ready-to-read format, no compilation needed

Getting Started

Viewing Lecture Notes

1. For .tmu format (recommended):

   • Mogan: Open-source scientific editor with full .tmu support
   • Liii STEM: Professional scientific text editor with AI assistance
   • Navigate to cs229t_fall_2018/tmu/
   • Use Mogan or Liii STEM to open .tmu files

2. For PDF format:

   • Navigate to cs229t_fall_2018/pdfs/
   • Open any .pdf file with your preferred PDF reader

Recommended Tools

Liii STEM - Professional Scientific Editor

• Website: https://liiistem.cn/
• Features:
  • ✨ Magic paste functionality for seamless content import
  • 📚 AI-powered interactive notebook
  • 🔢 Intuitive mathematical formula editing
  • 📁 Multi-format import/export support
  • 🔌 Extensible plugin ecosystem
  • ⚡ 100x faster formula editing compared to traditional LaTeX

Mogan - Open Source Alternative

• GitHub: https://github.com/XmacsLabs/mogan
• Features:
  • Free and open-source
  • Full .tmu format support
  • Cross-platform compatibility
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Course Information

• Course: CS229T - Statistical Learning Theory
• Instructor: Professor Tengyu Ma
• Institution: Stanford University
• Term: Fall 2018
• Official Course Page: https://web.stanford.edu/class/cs229t/

Additional Resources

The lectures/ directory contains:

• Outline format notes (notes.otl)
• Conversion utilities (otl2tex, otl2pdf)
• Build automation (Makefile)
• JavaScript utilities for web-based viewing

Contributing

This repository contains course materials from Stanford University. The .tmu format is transferred by Liii Network. If you find any errors or have suggestions for improvements, please feel free to open an issue or submit a pull request.

License

This repository contains educational materials from Stanford University. Please respect the original course materials and use them for educational purposes only.

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Note: These are student scribe notes and may contain errors. For the most accurate information, please refer to the official course materials and lectures.