Fundamentals of Reinforcement Learning

Overview

Delve into the core of AI with the University of Alberta's "Fundamentals of Reinforcement Learning" course, available on Coursera. Reinforcement Learning (RL) stands at the intersection of Machine Learning and automated decision-making, emerging as a critical area of study for those keen on advancing intelligent systems. This course provides a robust introduction to the field, equipping you with the skills to frame problems as Markov Decision Processes (MDPs), navigate the crucial exploration/exploitation tradeoff, leverage value functions for optimal decision outcomes, and implement dynamic programming for industrial control issues.

By the end of this course, you'll grasp the essential concepts of Reinforcement Learning, including classic and contemporary algorithms, enabling you to apply RL solutions to real-world problems with clear MDP specifications. Targeted at those interested in Artificial Intelligence, Machine Learning, and specifically Reinforcement Learning, this course marks the beginning of the Reinforcement Learning Specialization offered through Coursera by the University of Alberta. Embark on this learning journey to transform into a proficient user of Reinforcement Learning, opening doors to innovative interactive agents and intelligent decision-making strategies relevant in today's technologically driven landscape.

Categories: Artificial Intelligence Courses, Machine Learning Courses, Reinforcement Learning Courses.

Syllabus

Taught by

Martha White and Adam White

Tags

canada