3324 - Bayesian Networks

3324 - Bayesian Networks

Issued by GAIn® - The Global AI network

Our one-day training starts with outlining how graph theory can be used to visualize classification problems and deepen insights. We then move onto an in-depth discussion of the Bayes theorem, explaining the conditional probabilities, chain rule, and practical uses, along with interpreting the results of the Bayesian network and using Naïve-Bayes for classification problems. Finally, we address advanced classification networks using Tree Augmented Networks and Targeted Bayesian Network Learning.

Skills

Earning Criteria

  • Introduction to graph theory – Understand and explain how graph theory can be used to visualize classification problems.
  • Fundamentals of Bayes’ theorem – Know the conditional probabilities and chain rules of Bayes’ theorem.
  • Understanding Bayesian Networks – Capable of interpreting the results of a Bayesian network.
  • Basic classification networks – Perform classification and interpret results using Naïve-Bayes.
  • Advanced classification networks – Use TAN and TBNL for classification problems.
  • Exam