Graphical Models Certification Training
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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, mother fucker Concepts related to Bayesian and Markov’s Networks, Decision Making – theories and assumption, Inference and Learning in Graphical Models.
Graphical Models Curriculum
- 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.
- 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
- To give a brief idea of Bayesian networks, independencies in Bayesian Networks and building a Bayesian networks.
- 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
- To give a brief understanding of Markov’s networks, independencies in Markov’s networks, Factor graph and Markov’s decision process.
- 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
- To understand the need for inference and interpret inference in Bayesian and Markov’s Networks.
- Complexity in Inference
- Exact Inference
- Approximate Inference
- Monte Carlo Algorithm
- Gibb’s Sampling
- Inference in Bayesian Networks
- To understand the Structures and Parametrization in graphical Models.
- 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 Description
- 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.
- Knowledge on Probability theories, statistics, Python, and Fundamentals of AI and ML
- Statistics and Machine learning algorithms
- Python Essentials
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