Networking and Graphical Models in Machine Learning
Course Summary
This course provides a comprehensive introduction to networking concepts and graphical models in machine learning, focusing on probabilistic graphical models (PGMs), Bayesian networks, and Markov networks. Participants will learn how to model dependencies, perform inference, and apply these techniques in real-world scenarios such as social network analysis, recommendation systems, and decision-making under uncertainty. The course also covers graph neural networks (GNNs) for deep learning applications in network-based data.
Key Learning Outcomes
- Understand graph-based representations of structured data and their applications.
- Learn Bayesian networks, Markov networks, and Hidden Markov Models (HMMs).
- Perform inference and probabilistic reasoning with graphical models.
- Explore graph neural networks (GNNs) for deep learning applications.
- Apply network-based models in fraud detection, bioinformatics, and social network analysis.
