About this Course

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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. 15 hours to complete
English

What you will learn

  • Formalize problems as Markov Decision Processes

  • Understand basic exploration methods and the exploration / exploitation tradeoff

  • Understand value functions, as a general-purpose tool for optimal decision-making

  • Know how to implement dynamic programming as an efficient solution approach to an industrial control problem

Skills you will gain

  • Artificial Intelligence (AI)
  • Machine Learning
  • Reinforcement Learning
  • Function Approximation
  • Intelligent Systems
Flexible deadlines
Reset deadlines in accordance to your schedule.
Shareable Certificate
Earn a Certificate upon completion
100% online
Start instantly and learn at your own schedule.
Intermediate Level

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

Approx. 15 hours to complete
English

Offered by

Placeholder

University of Alberta

Placeholder

Alberta Machine Intelligence Institute

Syllabus - What you will learn from this course

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Week
1
Week 1
1 hour to complete

Welcome to the Course!

1 hour to complete
4 videos (Total 20 min), 2 readings
4 hours to complete

An Introduction to Sequential Decision-Making

4 hours to complete
8 videos (Total 46 min), 3 readings, 2 quizzes
Week
2
Week 2
3 hours to complete

Markov Decision Processes

3 hours to complete
7 videos (Total 36 min), 2 readings, 2 quizzes
Week
3
Week 3
3 hours to complete

Value Functions & Bellman Equations

3 hours to complete
9 videos (Total 56 min), 3 readings, 2 quizzes
Week
4
Week 4
4 hours to complete

Dynamic Programming

4 hours to complete
10 videos (Total 72 min), 3 readings, 2 quizzes

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About the Reinforcement Learning Specialization

Reinforcement Learning

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