Decision Making Under Uncertainty using POMDPs.jl

"Decision Making Under Uncertainty using POMDPs.jl" is a comprehensive course that explores the concept of decision-making in the face of uncertainty. The course focuses on the use of Partially Observable Markov Decision Processes (POMDPs), a mathematical framework for decision-making that has become increasingly popular in recent years due to its ability to model real-world problems with incomplete information. The course begins with an overview of POMDPs and their applications in various fields such as robotics, finance, healthcare, and transportation. Students will learn how to model decision problems using POMDPs and the importance of uncertainty in decision-making. The course will then delve into the technical aspects of POMDPs, providing students with a thorough understanding of the algorithms and techniques used to solve POMDPs. Students will learn about the Bellman equations, the value iteration algorithm, and the Monte Carlo tree search algorithm, which are commonly used techniques for solving POMDPs. The course will also cover how to use POMDPs.jl, a Julia package for implementing POMDPs, to model and solve real-world decision problems. In addition to the technical aspects of POMDPs, the course will also cover the practical applications of POMDPs in various fields. Students will learn about how POMDPs have been used to solve real-world problems such as robotic navigation, autonomous driving, and medical diagnosis. Throughout the course, students will work on practical assignments and projects, applying the concepts and techniques they have learned to solve real-world problems. By the end of the course, students will have a solid understanding of how POMDPs can be used to model and solve decision problems under uncertainty. Overall, "Decision Making Under Uncertainty using POMDPs.jl" is an essential course for anyone interested in decision-making under uncertainty. The course provides a solid foundation in POMDPs and their applications, and students will gain practical experience applying these techniques to real-world problems. Whether you are a researcher, a practitioner, or a student, this course will equip you with the tools and knowledge you need to tackle decision problems under uncertainty. Author: The Julia Programming Language