Learning Objectives: 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

Learning Objectives: 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

Learning Objectives: 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

Learning Objectives: 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

Learning Objectives: 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

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.

People 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.

Required Pre-requisites:

• Knowledge on Probability theories, statistics, Python, and Fundamentals of AI and ML

CertAdda offers you complimentary self-paced courses

• Statistics and Machine learning algorithms
• Python Essentials

The system requirement is a system with an Intel i3 processor or above, minimum 3GB RAM (4GB recommended) and an operating system either of 32bit or 64bit.

Cloud Lab has been provided to ensure you get real-time hands-on experience to practice your new skills on a pre-configured environment.

You will never miss a lecture at CertAdda You can choose either of the two options: View the recorded session of the class available in your LMS or You can attend the missed session, in any other live batch.

Your access to the Support Team is for lifetime and will be available 24/7. The team will help you in resolving queries, during and after the course.

Post-enrolment, the LMS access will be instantly provided to you and will be available for lifetime. You will be able to access the complete set of previous class recordings, PPTs, PDFs, assignments. Moreover the access to our 24×7 support team will be granted instantly as well. You can start learning right away.

Yes, the access to the course material will be available for lifetime once you have enrolled into the course.