Bayesian Networks ★★★★ Master Level
Are you looking to deepen your insights on which variables influence each other? Or do you want to use a model that allows you to incorporate expert knowledge with a state-of-the-art algorithm? Join our deep-dive on Bayesian Networks, where we’ll address essential concepts of Bayesian thinking, network model basics, and model variable interactions.
Supervised Machine Learning
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Through our UMCG - MIcompany collaboration, we aim to accelerate medical discoveries with innovative AI techniques. Learned a lot!
Professor and Lung Pediatrician at UMCG
- How graph theory works
- The conditional probabilities and chain rules of the Bayes theorem
- How to interpret a Bayesian network
- The Naïve-Bayes model
- Advanced classification networks TAN and TBNL
- 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