[University of Alberta] Reinforcement Learning Specialization

Alpha Zero, the programme that beat the world’s best chess player, is among the most well-known examples of what is called Reinforcement Learning (RL). The foundational principles of Reinforcement Learning transfer to cutting-edge technology skills and apply to a wide range of use cases across industries.

This four-course Specialization is intended for learners with at least one year of computer science undergraduate education or professionals with 2-3 years of software development experience. Academics, industry practitioners, and computer scientists will be able to advance their foundational machine learning knowledge with these courses and master the tools necessary to build systems that create automated decisions.

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.

Time Commitment

2-3 hours/week over 4-6 months

Prerequisites

Experience and comfort 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).

Course Content

There are 4 courses in this Specialization. The tools learned can be applied to AI in game development, Google assistants, clinical decision making, industrial process control, finance portfolio balancing, oil & gas pipelines and more.

Course 1: Fundamentals of Reinforcement Learning

Course 2: Sample Based Learning Methods

Course 3: Prediction and Control with Function Approximation

Course 4: A Complete Reinforcement Learning System (Capstone)

Instructors

Adam White, Assistant Professor
Adam White is an Assistant Professor in the Department of Computing Sciences at the University of Alberta, Faculty of Science, and a Senior Research Scientist at DeepMind. Adam's research focuses on the problem of Artificial Intelligence, specifically how to replicate or simulate human-level intelligence in physical and simulated agents.

His research program explores how the problem of intelligence can be modelled as a reinforcement learning agent interacting with some unknown environment, learning from a scalar reward signal rather than explicit feedback. Adam has taught Reinforcement Learning and Artificial Intelligence at the graduate and undergraduate levels, at both the University of Alberta and Indiana University. Outside of teaching and research Adam spends his time playing Gaelic Football, and exploring the natural world.

Martha White, Assistant Professor
Martha White is an Assistant Professor in the Department of Computing Sciences at the University of Alberta, Faculty of Science. Her research focus is on developing algorithms for agents continually learning on streams of data, with an emphasis on representation learning and reinforcement learning. Martha is a PI of AMII—the Alberta Machine Intelligence Institute and a director of RLAI—the Reinforcement Learning and Artificial Intelligence Lab at the University of Alberta. She enjoys soccer, the outdoors, cooking and especially reading sci-fi.