probml/pml-book
Pml Book
This repository contains the digital textbook and supplementary materials for probabilistic machine learning education. It provides structured text and guided study materials covering the mathematical foundations of probability and neural networks.
The project emphasizes reproducibility through a collection of interactive notebooks and standalone scripts used to recreate data plots and figures from the text. These materials are hosted in external environments to allow users to execute complex machine learning code without local installation.
The educational surface includes lecture slides, exercise solutions, and supplementary documents that provide additional technical details. Content is organized using a markdown-driven structure and managed via version control to maintain consistency across book editions.
Features
- Probabilistic Machine Learning Study Guides - Provides structured text and supplementary guides for mastering topics from basic probability to neural networks.
- Markdown Content Structures - Organizes educational text into hierarchical markdown files processed by a static site generator.
- Notebook-Based Experimentation - Provides interactive notebooks that combine code and documentation to generate the figures and computations described in the text.
- Educational Code Repositories - Provides source code collections specifically curated to accompany the educational textbook.
- Educational Content - Offers a comprehensive collection of text and supplemental materials for study and reference.
- Educational Examples - Includes illustrative code samples and interactive notebooks designed for instructional purposes.
- Probabilistic Programming - Provides educational resources and code demonstrations for implementing probabilistic programming concepts.
- AI & Machine Learning Education - Provides educational content covering neural network theory and the mathematical foundations of probability.
- Notebook-Based Figure Generation - Executes interactive Python cells to compute data and render visual plots used throughout the text.
- Figure Recreation - Allows users to execute code to exactly reproduce visual results and plots presented in the technical text.
- Notebook Environment Integrations - Integrates with cloud notebook platforms to allow readers to execute code without local dependency installation.
- Study Materials - Provides exercise solutions and lecture slides to support the theoretical study of probabilistic machine learning.
- Machine Learning Lectures - Provides academic lectures and theoretical explanations to support the study of probabilistic models.
- Supplementary Technical Documents - Provides downloadable documents containing additional technical details and extended content for the core series.
- Reproducible Research Documents - Ships interactive notebooks and scripts that pair narrative text with executable code to recreate textbook figures.
- Educational Computational Scripts - Provides specialized scripts to regenerate figures and perform computations described in the text.
- Machine Learning - Comprehensive probabilistic machine learning textbook covering core concepts.
Star history
How this analysis was created: This summary and feature list were written by an AI model that read the project's README and public documentation pages. Each feature links to the documentation it came from; stars, license and language come straight from the GitHub API. The model does not read the source code, and the analysis is refreshed when the project is re-analysed. Learn more on our About page.
