Mastering AI Decision-Making: Probabilistic Models & Uncertainty

What you will learn:

Probability Theory
Bayesian Networks
Conditional Probability & Independence
Probabilistic Graphical Models
Markov Chains
Hidden Markov Models
Python Implementation of Probabilistic Models
Decision-Making Under Uncertainty
Anaconda & Jupyter Notebook Usage
Applications of Bayesian Networks and Markov Models

Description

Navigate the complexities of AI decision-making in uncertain environments. This comprehensive course equips you with the foundational knowledge and practical skills to build robust AI systems. We delve into the core concepts of probability, conditional independence, and probabilistic graphical models, providing you with a solid understanding of how to represent and reason with uncertainty. You'll master Bayesian Networks, exploring their applications in various fields, from aviation to medical diagnosis. Furthermore, we'll explore the interplay of time and uncertainty using Markov Chains and Hidden Markov Models, learning how to model dynamic systems and make predictions under conditions of incomplete information.

Through hands-on exercises and practical implementations using Python, you'll gain confidence in applying these powerful techniques. This isn't just theory; it's a practical, step-by-step journey to building intelligent systems capable of handling real-world challenges. You will learn to install and utilize necessary software, such as Anaconda and Jupyter Notebooks, ensuring a seamless learning experience. By the end of this course, you'll be equipped with the knowledge and tools necessary to design intelligent agents capable of making informed decisions even in the face of uncertainty. Join now and unlock the potential of probabilistic AI!

Curriculum

Course Introduction

This introductory section sets the stage for the course. The "Course Introduction" lecture provides an overview of the course's goals and learning objectives (2:59). The "Course Outline" lecture details the structure and content of the course, outlining what you will learn in each section (1:35).

Actions Under Uncertainty

This section lays the groundwork for understanding and handling uncertainty in AI. The lectures cover fundamental concepts such as representing uncertainty, probability notation, independence, and conditional independence (total duration: approximately 38 minutes, including interactive questions). You'll learn to define and approach decision-making problems where uncertainty is a primary factor.

Software Installation

This section guides you through the setup of the necessary software environment for the course's practical exercises. Lectures cover the installation of Anaconda distribution and detailed instructions on effectively using Jupyter Notebooks across five lectures (total duration: approximately 20 minutes). These instructions ensure you are ready to implement the theoretical concepts in practice.

Bayesian Networks

This section delves into Bayesian Networks, a powerful tool for representing probabilistic relationships. Lectures cover Bayes' Theorem, the construction of Bayesian Networks, implementation, inference methods, and practical applications (total duration: approximately 60 minutes, including interactive questions). You'll learn how to build and use Bayesian Networks for problem-solving.

Time and Uncertainty

This section explores how time affects decision-making under uncertainty. The lectures introduce Markov Chains and Hidden Markov Models (HMMs) – crucial concepts for modeling dynamic systems. You'll learn their implementation in Python and apply them to real-world scenarios. This section includes practical exercises and a total duration of approximately 60 minutes (including interactive questions).

Course Conclusion

This final section summarizes the course, highlighting key takeaways and offering insights into further learning opportunities (2:03).