Foundations of A.I.: Actions Under Uncertainty

Uncertainty in Artificial Intelligence: Mastering Decision-Making with Probabilistic Models

Navigating the complexities of the real world requires dealing with uncertainty, and Artificial Intelligence (AI) is no exception. This course provides a comprehensive introduction to designing AI programs and utilizing probabilistic graphical models for effective decision-making when faced with incomplete information and unpredictable environments.

Understanding Uncertainty in AI

Uncertainty is a fundamental challenge in AI. Real-world scenarios are often complex, dynamic, and ambiguous, demanding robust approaches to decision-making. This course delves into the causes of uncertainty and explores methods for representing and measuring it.

The Power of Probability

Probability serves as the core measurement of uncertainty. We'll build a strong foundation in probability theory through a series of lectures, covering key concepts and theorems.

Key Topics Covered:

• Probability Theorem Foundations: A deep dive into the essential principles of probability.
• Bayes' Theorem: Learn to update beliefs based on new evidence – a cornerstone of probabilistic reasoning.
• Bayesian Networks: Discover how Bayesian Networks represent conditional independence, offering a powerful framework for modeling complex relationships. These networks are widely applied in fields like:
  • Aviation Industry: For risk assessment and predictive maintenance.
  • Business Intelligence: For forecasting and strategic planning.
  • Medical Diagnosis: For improving accuracy and efficiency.
  • Public Policy: For informed decision-making in complex situations.

Decision-Making Under Uncertainty & Time

The second half of the course explores the interplay between time and uncertainty in decision-making. We'll investigate the Markov property and its practical applications, enabling you to model systems that evolve over time.

Why is this important?

Representing uncertainty and developing computational models to solve for it is a critical skill in modern AI. This course equips you with the knowledge and tools to build more resilient, adaptable, and intelligent systems.