About this Course
10,296 recent views

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. 17 hours to complete

Suggested: 4-6 hours/week...

English

Subtitles: English

Skills you will gain

Artificial Intelligence (AI)Machine LearningReinforcement LearningFunction ApproximationIntelligent Systems

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. 17 hours to complete

Suggested: 4-6 hours/week...

English

Subtitles: English

Syllabus - What you will learn from this course

Week
1
4 minutes to complete

Welcome to the Course!

1 video (Total 4 min)
1 video
Week
2
6 hours to complete

On-policy Prediction with Approximation

12 videos (Total 61 min), 2 quizzes
12 videos
The Value Error Objective4m
Introducing Gradient Descent7m
Gradient Monte for Policy Evaluation5m
State Aggregation with Monte Carlo7m
Semi-Gradient TD for Policy Evaluation3m
Comparing TD and Monte Carlo with State Aggregation4m
The Linear TD Update3m
The True Objective for TD5m
Week 1 Summary4m
1 practice exercise
On-policy Prediction with Approximation30m
Week
3
5 hours to complete

Constructing Features for Prediction

10 videos (Total 43 min), 2 quizzes
10 videos
Using Tile Coding in TD4m
What is a Neural Network?3m
Non-linear Approximation with Neural Networks4m
Deep Neural Networks3m
Gradient Descent for Training Neural Networks8m
Optimization Strategies for NNs4m
Week 2 Review2m
1 practice exercise
Constructing Features for Prediction28m
Week
4
5 hours to complete

Control with Approximation

6 videos (Total 28 min), 1 reading, 2 quizzes
6 videos
Exploration under Function Approximation3m
Average Reward: A New Way of Formulating Control Problems10m
Week 3 Review2m
1 reading
Weekly Reading40m
1 practice exercise
Practice Quiz

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....

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.

More questions? Visit the Learner Help Center.