About this Specialization
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100% online courses

Start instantly and learn at your own schedule.

Flexible Schedule

Set and maintain flexible deadlines.

Intermediate Level

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

Approx. 3 months to complete

Suggested 11 hours/week

English

Subtitles: English

What you will learn

  • Check

    Build a Reinforcement Learning system for sequential decision making.

  • Check

    Understand the space of RL algorithms (Temporal- Difference learning, Monte Carlo, Sarsa, Q-learning, Policy Gradients, Dyna, and more).

  • Check

    Understand how to formalize your task as a Reinforcement Learning problem, and how to begin implementing a solution.

  • Check

    Understand how RL fits under the broader umbrella of machine learning, and how it complements deep learning, supervised and unsupervised learning 

Skills you will gain

Artificial Intelligence (AI)Machine LearningReinforcement LearningFunction ApproximationIntelligent Systems

100% online courses

Start instantly and learn at your own schedule.

Flexible Schedule

Set and maintain flexible deadlines.

Intermediate Level

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

Approx. 3 months to complete

Suggested 11 hours/week

English

Subtitles: English

How the Specialization Works

Take Courses

A Coursera Specialization is a series of courses that helps you master a skill. To begin, enroll in the Specialization directly, or review its courses and choose the one you'd like to start with. When you subscribe to a course that is part of a Specialization, you’re automatically subscribed to the full Specialization. It’s okay to complete just one course — you can pause your learning or end your subscription at any time. Visit your learner dashboard to track your course enrollments and your progress.

Hands-on Project

Every Specialization includes a hands-on project. You'll need to successfully finish the project(s) to complete the Specialization and earn your certificate. If the Specialization includes a separate course for the hands-on project, you'll need to finish each of the other courses before you can start it.

Earn a Certificate

When you finish every course and complete the hands-on project, you'll earn a Certificate that you can share with prospective employers and your professional network.

how it works

There are 4 Courses in this Specialization

Course1

Fundamentals of Reinforcement Learning

4.7
279 ratings
81 reviews
Course2

Sample-based Learning Methods

4.8
73 ratings
21 reviews
Course3

Prediction and Control with Function Approximation

4.8
12 ratings
2 reviews
Course4

A Complete Reinforcement Learning System (Capstone)

Instructors

Avatar

Martha White

Assistant Professor
Computing Science
Avatar

Adam White

Assistant Professor
Computing Science

About University of Alberta

UAlberta is considered among the world’s leading public research- and teaching-intensive universities. As one of Canada’s top universities, we’re known for excellence across the humanities, sciences, creative arts, business, engineering and health sciences....

About Alberta Machine Intelligence Institute

The Alberta Machine Intelligence Institute (Amii) is home to some of the world’s top talent in machine intelligence. We’re an Alberta-based research institute that pushes the bounds of academic knowledge and guides business understanding of artificial intelligence and machine learning....

Frequently Asked Questions

  • Yes! To get started, click the course card that interests you and enroll. You can enroll and complete the course to earn a shareable certificate, or you can audit it to view the course materials for free. When you subscribe to a course that is part of a Specialization, you’re automatically subscribed to the full Specialization. Visit your learner dashboard to track your progress.

  • This course is completely online, so there’s no need to show up to a classroom in person. You can access your lectures, readings and assignments anytime and anywhere via the web or your mobile device.

  • It is recommended that learners take between 4-6 months to complete the specialization.

  • Recommended that learners have at least one year of undergraduate computer science or 2-3 years of professional experience in software development. Experience and comfort with programming in Python required. Must be comfortable converting algorithms and pseudocode into Python. Basic understanding of concepts from statistics (distributions, sampling, expected values), linear algebra (vectors and matrices), and calculus (computing derivatives)

  • Yes, it is recommended that courses are taken sequentially.

  • Learners that complete the specialization will earn a Coursera specialization certificate signed by the professors of record, not a University of Alberta credit.

  • By the end of this specialization, you will be able to"

    • Build a Reinforcement Learning system for sequential decision making.
    • Understand the space of RL algorithms (Temporal- Difference learning, Monte Carlo, Sarsa, Q-learning, Policy Gradients, Dyna, and more).
    • Understand how to formalize your task as a Reinforcement Learning problem, and how to begin implementing a solution.
    • Understand how RL fits under the broader umbrella of machine learning, and how it complements deep learning, supervised and unsupervised learning 

More questions? Visit the Learner Help Center.