About this Course

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Shareable Certificate

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Flexible deadlines

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Intermediate Level

Probabilities & Expectations, basic linear algebra, basic calculus, Python 3.0 (at least 1 year), implementing algorithms from pseudocode.

Approx. 23 hours to complete

English

Subtitles: English

Skills you will gain

Artificial Intelligence (AI)Machine LearningReinforcement LearningFunction ApproximationIntelligent Systems

Shareable Certificate

Earn a Certificate upon completion

100% online

Start instantly and learn at your own schedule.

Flexible deadlines

Reset deadlines in accordance to your schedule.

Intermediate Level

Probabilities & Expectations, basic linear algebra, basic calculus, Python 3.0 (at least 1 year), implementing algorithms from pseudocode.

Approx. 23 hours to complete

English

Subtitles: English

Offered by

University of Alberta logo

University of Alberta

Alberta Machine Intelligence Institute logo

Alberta Machine Intelligence Institute

Syllabus - What you will learn from this course

Week
1

Week 1

1 hour to complete

Welcome to the Final Capstone Course!

1 hour to complete
2 videos (Total 10 min), 2 readings
2 videos
Meet your instructors!8m
2 readings
Reinforcement Learning Textbook10m
Pre-requisites and Learning Objectives10m
Week
2

Week 2

1 hour to complete

Milestone 1: Formalize Word Problem as MDP

1 hour to complete
4 videos (Total 23 min)
4 videos
Andy Barto on What are Eligibility Traces and Why are they so named?9m
Let's Review: Markov Decision Processes6m
Let's Review: Examples of Episodic and Continuing Tasks3m
Week
3

Week 3

1 hour to complete

Milestone 2: Choosing The Right Algorithm

1 hour to complete
7 videos (Total 40 min)
7 videos
Let's Review: Expected Sarsa3m
Let's Review: What is Q-learning?3m
Let's Review: Average Reward- A New Way of Formulating Control Problems10m
Let's Review: Actor-Critic Algorithm5m
Csaba Szepesvari on Problem Landscape8m
Andy and Rich: Advice for Students5m
1 practice exercise
Choosing the Right Algorithm
Week
4

Week 4

1 hour to complete

Milestone 3: Identify Key Performance Parameters

1 hour to complete
4 videos (Total 25 min)
4 videos
Let's Review: Non-linear Approximation with Neural Networks4m
Drew Bagnell on System ID + Optimal Control6m
Susan Murphy on RL in Mobile Health7m
1 practice exercise
Impact of Parameter Choices in RL40m

About the Reinforcement Learning Specialization

The Reinforcement Learning Specialization consists of 4 courses exploring the power of adaptive learning systems and artificial intelligence (AI). Harnessing the full potential of artificial intelligence requires adaptive learning systems. Learn how Reinforcement Learning (RL) solutions help solve real-world problems through trial-and-error interaction by implementing a complete RL solution from beginning to end. By the end of this Specialization, learners will understand the foundations of much of modern probabilistic artificial intelligence (AI) and be prepared to take more advanced courses or to apply AI tools and ideas to real-world problems. This content will focus on “small-scale” problems in order to understand the foundations of Reinforcement Learning, as taught by world-renowned experts at the University of Alberta, Faculty of Science. The tools learned in this Specialization can be applied to game development (AI), customer interaction (how a website interacts with customers), smart assistants, recommender systems, supply chain, industrial control, finance, oil & gas pipelines, industrial control systems, and more....
Reinforcement Learning

Frequently Asked Questions

  • Once you enroll for a Certificate, you’ll have access to all videos, quizzes, and programming assignments (if applicable). Peer review assignments can only be submitted and reviewed once your session has begun. If you choose to explore the course without purchasing, you may not be able to access certain assignments.

  • When you enroll in the course, you get access to all of the courses in the Specialization, and you earn a certificate when you complete the work. Your electronic Certificate will be added to your Accomplishments page - from there, you can print your Certificate or add it to your LinkedIn profile. If you only want to read and view the course content, you can audit the course for free.

  • If you subscribed, you get a 7-day free trial during which you can cancel at no penalty. After that, we don’t give refunds, but you can cancel your subscription at any time. See our full refund policy.

  • Yes, Coursera provides financial aid to learners who cannot afford the fee. Apply for it by clicking on the Financial Aid link beneath the "Enroll" button on the left. You'll be prompted to complete an application and will be notified if you are approved. You'll need to complete this step for each course in the Specialization, including the Capstone Project. Learn more.

  • This Course doesn't carry university credit, but some universities may choose to accept Course Certificates for credit. Check with your institution to learn more. Online Degrees and Mastertrack™ Certificates on Coursera provide the opportunity to earn university credit.

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