Reinforcement Learning offered by University of Alberta
- Public University
1 Campus- Estd. 1908
Reinforcement Learning at UofA Overview
Mode of learning | Online |
Course Level | UG Certificate |
Reinforcement Learning at UofA Highlights
- Learn statistical learning techniques
- Learn about several algorithms
- Learn how to solve problems with large, high-dimensional, and potentially infinite state spaces
Reinforcement Learning at UofA Course details
- Build a RL system that knows how to make automated decisions
- Understand how RL relates and fits into the broader umbrella of machine learning, deep learning, supervised and unsupervised learning
- Understand the space of RL algorithms (Temporal Difference learning, Monte Carlo, Sarsa, Q-learning, Policy Gradient, Dyna, and more)
- Understand how to formalize your task as a RL problem, and how to begin implementing a solution
- The Reinforcement Learning Specialization consists of 4 courses exploring the power of adaptive learning systems and artificial intelligence (AI).
- By the end of this Specialization, learners will understand the foundations of much of modern probabilistic 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
Reinforcement Learning at UofA Curriculum
Course 1 - Fundamentals of Reinforcement Learning
Module 0: Welcome to the Course
Module 1: The K-Armed Bandit Problem
Module 2: Markov Decision Processes
Module 3: Value Functions & Bellman Equations
Module 4: Dynamic Programming
Course 2 - Sample Based Learning Methods
Module 0: Introduction to the course
Module 1: Monte Carlo Methods for Prediction & Control
Module 2: Temporal Difference Learning Methods for Prediction
Module 3: Temporal Difference Learning Methods for Control
Module 4: Planning, Learning, & Acting
Course 3 - Prediction and Control with Function Approximation
Module 0: Introduction to the course
Module 1: On-policy Prediction with Approximation
Module 2: Construction Features for Prediction
Module 3: Control with Approximation
Module 4: Policy Gradient
Course 4 - A Complete Reinforcement Learning System (Capstone)
Module 0: Welcome to the Capstone
Module 1: Milestone 1: Formalize Word Problem as MDP
Module 2: Milestone 2: Choosing the Right Algorithm
Module 3: Milestone 3: Identify Key Performance Parameters
Module 4: Milestone 4: Implement Your Agent
Module 5: Submit Your Parameter Study and Course Wrap Up
Reinforcement Learning at UofA Entry Requirements
- 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)
- Not mentioned
Other courses offered by UofA
Study in CanadaUniversities in CanadaUniversities in EdmontonUniversity of AlbertaReinforcement LearningReinforcement Learning at UofA Popular & recent articles
Reinforcement Learning at UofA Contact Information
116 St. and 85 Ave., Edmonton, AB, Canada T6G 2R3
Edmonton ( Alberta)