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Fundamentals of Reinforcement Learning

Master machine learning techniques with focus on reinforcement learning algorithms, value functions, and dynamic programming. Perfect for AI enthusiasts.

Master machine learning techniques with focus on reinforcement learning algorithms, value functions, and dynamic programming. Perfect for AI enthusiasts.

This course cannot be purchased separately - to access the complete learning experience, graded assignments, and earn certificates, you'll need to enroll in the full Reinforcement Learning Specialization program. You can audit this specific course for free to explore the content, which includes access to course materials and lectures. This allows you to learn at your own pace without any financial commitment.

4.8

(2,757 ratings)

91,471 already enrolled

Instructors:

Martha White

Martha White

Adam White

Adam White

English

پښتو, বাংলা, اردو, 3 more

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Fundamentals of Reinforcement Learning

This course includes

15 Hours

Of Self-paced video lessons

Intermediate Level

Completion Certificate

awarded on course completion

Free

What you'll learn

  • Formalize problems as Markov Decision Processes

  • Understand basic exploration methods and exploration/exploitation tradeoff

  • Master value functions for optimal decision-making

  • Implement dynamic programming for industrial control

  • Apply RL algorithms to real-world problems

Skills you'll gain

Reinforcement Learning
Machine Learning
Artificial Intelligence
Dynamic Programming
MDP
Value Functions
Policy Optimization
Algorithm Implementation
Python Programming
Statistical Learning

This course includes:

3.7 Hours PreRecorded video

5 quizzes

Access on Mobile, Tablet, Desktop

FullTime access

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There are 5 modules in this course

This comprehensive course introduces the fundamentals of Reinforcement Learning, a powerful subset of Machine Learning focused on automated decision-making and AI. Students learn to formalize problems as Markov Decision Processes, understand exploration methods, master value functions for optimal decision-making, and implement dynamic programming solutions for industrial control problems. The course combines theoretical foundations with practical applications, preparing learners for real-world AI challenges.

Welcome to the Course!

Module 1 · 1 Hours to complete

An Introduction to Sequential Decision-Making

Module 2 · 3 Hours to complete

Markov Decision Processes

Module 3 · 3 Hours to complete

Value Functions & Bellman Equations

Module 4 · 3 Hours to complete

Dynamic Programming

Module 5 · 3 Hours to complete

Fee Structure

Instructors

Martha White

Martha White

4.7 rating

802 Reviews

99,327 Students

4 Courses

Innovator in Machine Learning and Reinforcement Learning

Martha White is an Assistant Professor in the Department of Computing Sciences at the University of Alberta, where she specializes in developing algorithms for agents that learn continuously from streams of data, focusing on representation learning and reinforcement learning. As a Principal Investigator at the Alberta Machine Intelligence Institute (AMII) and director of the Reinforcement Learning and Artificial Intelligence Lab (RLAI), she is at the forefront of research aimed at enhancing adaptive learning systems. Martha holds a Ph.D. in Computing Science from the University of Alberta and has published extensively on topics related to machine learning, including over 40 papers in top-tier journals and conferences. Her commitment to advancing knowledge in AI is complemented by her passion for mentoring emerging researchers and promoting diversity in computing. Outside of her academic pursuits, Martha enjoys soccer, outdoor activities, cooking, and reading science fiction, reflecting her diverse interests beyond the realm of technology.

Adam White

Adam White

4.7 rating

802 Reviews

99,327 Students

4 Courses

Leader in Reinforcement Learning and Artificial Intelligence

Adam White is an Assistant Professor in the Department of Computing Sciences at the University of Alberta and a Senior Research Scientist at DeepMind. His research centers on artificial intelligence, specifically on replicating or simulating human-level intelligence in both physical and simulated agents. Adam's work explores how intelligence can be modeled through reinforcement learning agents that interact with unknown environments, learning from scalar reward signals rather than explicit feedback. He has taught courses on Reinforcement Learning and Artificial Intelligence at both the University of Alberta and Indiana University, contributing to the education of future leaders in AI. As a Principal Investigator of the Reinforcement Learning and Artificial Intelligence Lab (RLAI) and a fellow at the Alberta Machine Intelligence Institute (AMII), Adam is dedicated to advancing the field through innovative research and practical applications. His contributions include developing new algorithms for reinforcement learning, creating the RL-Glue framework for reinforcement learning experiments, and demonstrating learning capabilities in mobile robots. Outside of academia, Adam enjoys playing Gaelic football and exploring the natural world, reflecting his diverse interests beyond his professional pursuits.

Fundamentals of Reinforcement Learning

This course includes

15 Hours

Of Self-paced video lessons

Intermediate Level

Completion Certificate

awarded on course completion

Free

Testimonials

Testimonials and success stories are a testament to the quality of this program and its impact on your career and learning journey. Be the first to help others make an informed decision by sharing your review of the course.

4.8 course rating

2,757 ratings

Frequently asked questions

Below are some of the most commonly asked questions about this course. We aim to provide clear and concise answers to help you better understand the course content, structure, and any other relevant information. If you have any additional questions or if your question is not listed here, please don't hesitate to reach out to our support team for further assistance.