3-day Virtual Seminar

From AI Theory to ML in Action: Building Your First Bayesian Model

Overview

This webinar bridges foundational AI theory and practical Machine Learning implementation by introducing Bayesian models and probabilistic reasoning. Participants will explore what Bayesian models are, how they differ from conventional ML models, and why probabilistic graphical models remain powerful tools in modern AI.

The session will include hands-on construction of a Bayesian Network using a simple dataset, followed by demonstrations of exact inference and approximate inference using practical query examples. By the end of the session, participants will understand how to model uncertainty, reason with probabilities, and implement Bayesian approaches in real-world scenarios.

Why you should Attend

Most Machine Learning practitioners rely only on standard predictive models without understanding probabilistic reasoning - limiting their ability to build interpretable and uncertainty-aware AI systems. Without knowledge of Bayesian models and inference techniques, you risk missing a powerful framework used in research, advanced AI systems, and real-world decision-making under uncertainty.

Areas Covered in the Session

• Introduction to Bayesian Thinking
  
• What is a Bayesian Model?
  
• Understanding Bayes’ Theorem in practice
  
• Bayesian models vs traditional Machine Learning models
  
• Probabilistic Graphical Models overview
  
• Structure and components of a Bayesian Network
  
• Real-world examples of Bayesian Networks
  
• Building a Bayesian Network on a simple dataset
  
• Understanding conditional dependencies
  
• Exact Inference:
  
  • Variable elimination / enumeration concept
    
  • Solving structured probability queries
    
• Approximate Inference:
  
  • Sampling-based approaches (conceptual overview)
    
• Demonstration of 3 inference queries
  
• Interpreting results and understanding uncertainty in AI systems

Who Will Benefit

• Intermediate-level Machine Learning practitioners
  
• AI & Data Science students
  
• Research-oriented learners
  
• Software developers transitioning into advanced AI
  
• Professionals preparing for AI research roles