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
Adam White
English
پښتو, বাংলা, اردو, 3 more
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
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
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
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.
Testimonials
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4.8 course rating
2,757 ratings
Frequently asked questions
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