About This Textbook¶
Overview¶
Machine Learning: Algorithms and Applications is an open-source intelligent textbook that provides a comprehensive introduction to machine learning for undergraduate students. It covers fundamental algorithms from K-Nearest Neighbors through deep learning, with an emphasis on both mathematical foundations and practical implementation.
What Makes This an Intelligent Textbook?¶
This textbook goes beyond static content by incorporating:
- Interactive MicroSims - 18 browser-based simulations that let you experiment with algorithms in real time
- Learning Graph - A concept dependency map that shows how topics relate to each other and recommends optimal learning paths
- Auto-graded Quizzes - Self-assessment questions for every chapter aligned to specific learning objectives
- Comprehensive Glossary - 199 terms with precise, standards-compliant definitions
- FAQ - 86 answers to common student questions
Technology Stack¶
| Component | Technology |
|---|---|
| Site Generator | MkDocs with Material theme |
| Simulations | p5.js, Chart.js, Plotly |
| Learning Graph | vis-network |
| Math Rendering | MathJax 3 |
| Hosting | GitHub Pages |
| Content Generation | Assisted by Claude Code |
Target Audience¶
- College undergraduate students in computer science or data science
- Prerequisites: linear algebra, calculus, and Python programming experience
- Self-learners interested in understanding ML algorithms from the ground up
License¶
This work is licensed under CC BY-NC-SA 4.0. You are free to share and adapt this material for non-commercial purposes with attribution.