Learning Bayesian Networks

This repo is a work in progress.

This is supplementary material to the book "Learning Bayesian Networks" by R. Neapolitan. We are going to look closely into the examples and exercises of chapters from the Book. There will be code for solving the examples and the excercies written in Python. For the purpose we will look into various open source libraries such as networkx, pomegranate, bnlearn, bayespy, and pgmpy and we will choose the most suitable library for implementing the examples and excerices.

Selected Topics in Machine Learning

• Reinforcement Learning and Game Theory

• Offline Reinforcement and Self-Supervised Learning

• NLP Concepts

• Transformers

• Large Language Models

• Bayesian Networks

• Probabilistic Machine Learning and Bayesian Methods

• Statistical Learning, Kernel Methods, Kolomogorov-Arnold Networks

• Optimization, Classification, Regression

• Causal Inference, Root Cause Analysis and Model Checking

• Deep Learning and Neural Networks

• Deep Learning for solving Image Processing problems and Generative Tasks

• Hypothesis Testing, Estimation of Treatment Effects and Generalized Synthetic Control

• Graphs and Dynamic Programming

• Dynamical Systems, State-Space Models, Chaos, Ergodicity

• Information Theory, Statistical Mechanics, Diffusion Models

• Queueing Networks, Queueing Theory, Reversible Stochastic Processes

• Spectral Analysis, Optimization in Spectral Domain, Spectral Domain Modeling

• Computability, Automata, Logic Systems, Formal Grammars and Theory of Parsing

• Thought Forming, Consciousness, Intelligent Machines, Semantic Inference

Note

This repository uses git Large File Storage feature. In order to download locally the large files (> 1MB) which are maintained by git LFS you will need to install the Git extension for versioning large files: https://git-lfs.com/