Executive Development Programme in Practical Probabilistic Graphical Models for Decision Making

This course is designed for senior executives and business leaders seeking to enhance their decision-making capabilities through the application of probabilistic graphical models (PGMs). Participants will gain a deep understanding of PGMs, including Bayesian networks and Markov models, and learn how to use these tools to analyze complex data, identify risks and opportunities, and make more informed strategic decisions.

Attendees will leave with practical skills to build and apply PGMs to real-world business challenges. The curriculum includes hands-on workshops and case studies, ensuring that participants can immediately apply their new knowledge to improve business outcomes.

1. 1. Introduction to Probabilistic Graphical Models: Learners will understand the basics of probabilistic graphical models (PGMs), including directed and undirected graphs, and gain foundational knowledge necessary for modeling real-world decision-making scenarios.
2. 2. Probabilistic Inference: This module will cover techniques for performing inference in PGMs, such as variable elimination, belief propagation, and sampling methods, enabling learners to make predictions and decisions under uncertainty.
3. 3. Bayesian Networks: Learners will delve into Bayesian networks, a type of PGM, studying their structure, parameters, and learning from data, with a focus on practical applications in decision support systems.
4. 4. Markov Random Fields: This module explores Markov random fields, discussing their properties, and how they can be used to model complex dependencies in data, providing learners with tools for advanced probabilistic modeling.
5. 5. Decision Theory and Utility Theory: Learners will study decision theory and utility theory to understand how to make optimal decisions under uncertainty, incorporating probabilistic graphical models into decision-making frameworks.
6. 6. Probabilistic Model Checking: This module covers techniques for checking the behavior of probabilistic models against desired properties, teaching learners how to use model checking to ensure the reliability of probabilistic systems.
7. 7. Machine Learning with PGMs: Focusing on the integration of machine learning techniques with PGMs, learners will learn to build and train models that can handle large datasets and complex relationships.
8. 8. Decision Making under Uncertainty: This module will explore advanced methods for decision making in uncertain environments, including expected utility theory and decision trees, equipping learners with strategies for robust decision-making.
9. 9. Advanced Probabilistic Graphical Models: Delving into more complex PGMs, such as hybrid models and deep probabilistic models, this module will prepare learners to tackle real-world problems that require sophisticated probabilistic representations.
10. 10. Practical Applications and Case Studies: Learners will apply their knowledge to real-world scenarios through case studies and projects, gaining hands-on experience with practical probabilistic graphical models for decision making in various industries.