A repo to gather every piece of work for the workshop.
Welcome to the Bayesian Networks Workshop GitHub repository! This repository contains materials for a comprehensive course on Bayesian Networks, including lecture notes, exercises, and a real-world case study. Whether you're new to Bayesian Networks or looking to deepen your understanding, this course is designed to help you master this powerful probabilistic modeling technique thanks to a Python library: pyAgrum.
- Introduction
- Course Content
- Getting Started
- Course Structure
- Exercises
- Real-World Case Study
- Contributing
- License
Bayesian Networks are a fundamental tool in the field of probabilistic graphical models. They are used to represent and reason about uncertain knowledge and are applied in various domains, including machine learning, artificial intelligence, and decision support systems. This course aims to provide you with a solid foundation in Bayesian Networks and equip you with practical skills to apply them to real-world problems.
The course is divided into several modules, each covering a specific aspect of Bayesian Networks. Here are some of the topics you will explore:
- Basics of Bayesian Networks
- Inference in Bayesian Networks
- Classification from a Bayesian Network
- Learning Bayesian Networks from Data
- Introduction to aGrUM/pyAgrum
To get started with the course, follow these steps: Clone this repository to your local machine using git clone. Review the course materials in the lectures directory. Complete the exercises provided in the exercises directory. Explore the real-world case study in the case_study directory.
The course is structured around a lecture, accompanied by a set of slides (cours_BN.pdf). The lectures are designed to provide you with a theoretical foundation for understanding Bayesian Networks.
The exercises directory contains a set of hands-on exercises and assignments to reinforce your understanding of Bayesian Networks and pyAgrum. These exercises cover various topics and levels of difficulty. Feel free to work through them at your own pace and use the provided solutions for self-assessment.
In the case_study directory, you will find a real-world case study that applies Bayesian Networks to a practical problem. This case study will give you an opportunity to see how Bayesian Networks can be used to model and solve complex, real-world scenarios.