Unlock the Power of Bayesian Networks:

Dive deep into probabilistic modeling and inference with practical exercises designed to elevate your data-driven decision-making skills.

You will learn about:

Foundations of Bayesian Networks
Understand the core concepts: probabilistic reasoning, conditional independence, and graphical representation of dependencies. 

Modeling and Inference Techniques
Learn how to construct Bayesian networks, perform parameter estimation, and apply inference algorithms for real-world decision-making.

Hands-On Implementation in Python
Explore practical examples using common Open-Source libraries to build, visualize, and query Bayesian networkshow to construct Bayesian networks.

Prerequisites:

• SIdeally a STEM-Background (Bachelor/Master)
• Hands-On: Laptop with Python installation (details will be provided in advance of the training)
• Hands-On: Basic understanding and, ideally, previous exposure to data science and programming

Who should attend:

• Data Scientists / Machine Learning Experts
• Automotive SOTIF responsible persons
• Functional Safety Engineers – who want to understand how they can model complex systems with uncertainty
• Technical Leads developing autonomous systems