Graphical Models Certification Course Overview
Graphical Models Course is designed to teach Graphical Models, fundamentals of Graphical Models, Probabilistic Theories, Types of Graphical Models – Bayesian (Directed) and Markov’s (Undirected) Networks, Representation of Bayesian and Markov’s Networks, Concepts related to Bayesian and Markov’s Networks, Decision Making – theories and assumption, Inference and Learning in Graphical Models.
This course is designed to:
- Give a brief idea about Graphical models, graph theory, probability theory, components of graphical models, types of graphical models, representation of graphical models, Introduction to inference, learning and decision making in Graphical Models.
- Give a brief idea of Bayesian networks, independencies in Bayesian Networks and building a Bayesian networks.
- Give a brief understanding of Markov’s networks, independencies in Markov’s networks, Factor graph and Markov’s decision process.
- Understand the need for inference and interpret inference in Bayesian and Markov’s Networks.
- Understand the Structures and Parametrization in graphical Models.
Who should go for this Graphical Models training?
Professionals who are interested/working in the Data Science field and have a basic idea of Machine Learning or Graphical Modelling, Researchers, Machine Learning and Artificial Intelligence enthusiasts.
What are the pre-requisites of taking this training?
