Introduction to Graphical Model

Goal: To provide a quick overview of Graphical models, graph theory, probability theory, components of graphical models, varieties of graphical models, and graphical model representation. 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 provide a quick overview of Bayesian networks, independencies in Bayesian networks, and the construction of 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 provide a quick overview of Markov's networks, Markov's network independencies, the 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 comprehend the need of inference and to interpret inference in Bayesian and Markov's networks.

Topics:

• Inference

• Complexity in Inference

• Exact Inference

• Approximate Inference

• Monte Carlo Algorithm

• Gibbs Sampling

• Inference in Bayesian Networks

Goal: To comprehend the need of inference and to interpret inference in Bayesian and Markov's networks.

Topics:

• Inference

• Complexity in Inference

• Exact Inference

• Approximate Inference

• Monte Carlo Algorithm

• Gibbs Sampling

• Inference in Bayesian Networks