Graphical Models Certification Training

Goal: 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.

Topics:

• Why do we need Graphical Models?
• Introduction to Graphical Model
• How does Graphical Model help you deal with uncertainty and complexity?
• Types of Graphical Models
• Graphical Modes
• Components of Graphical Model
• Representation of Graphical Models
• Inference in Graphical Models
• Learning Graphical Models
• Decision theory
• Applications

• Goal: To give a brief idea of Bayesian networks, independencies in Bayesian Networks and building a Bayesian networks.

Topics:

• What is Bayesian Network?
• Advantages of Bayesian Network for data analysis
• Bayesian Network in Python Examples
• Independencies in Bayesian Networks
• Criteria for Model Selection
• Building a Bayesian Network

Goal: To give a brief understanding of Markov’s networks, independencies in Markov’s networks, Factor graph and Markov’s decision process.

Topics:

• Example of a Markov Network or Undirected Graphical Model
• Markov Model
• Markov Property
• Markov and Hidden Markov Models
• The Factor Graph
• Markov Decision Process
• Decision Making under Uncertainty
• Decision Making Scenarios

Goal: To understand the need for inference and interpret inference in Bayesian and Markov’s Networks.

Topics:

• Inference
• Complexity in Inference
• Exact Inference
• Approximate Inference
• Monte Carlo Algorithm
• Gibb’s Sampling
• Inference in Bayesian Networks

Goal:To understand the Structures and Parametrization in graphical Models.

Topics:

• General Ideas in Learning
• Parameter Learning
• Learning with Approximate Inference
• Structure Learning
• Model Learning: Parameter Estimation in Bayesian Networks
• Model Learning: Parameter Estimation in Markov Networks