- Function Approximation
- Artificial Intelligence (AI)
- Reinforcement Learning
- Machine Learning
- Intelligent Systems
Reinforcement Learning
Completed by Sung-yeon LIM
August 30, 2020
Approximately 2 months at 10 hours a week to completeSung-yeon LIM's account is verified. Coursera certifies their successful completion of University of Alberta & Alberta Machine Intelligence Institute Reinforcement Learning Specialization.
Course Certificates Completed
Fundamentals of Reinforcement Learning
Sample-based Learning Methods
Prediction and Control with Function Approximation
A Complete Reinforcement Learning System (Capstone)
What you will learn
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
Skills you will gain
Course Certificates
Earned after completing each course in the Specialization
Fundamentals of Reinforcement Learning
University of Alberta & Alberta Machine Intelligence Institute
Taught by: Martha White & Adam White
Completed by: Sung-yeon LIM by April 23, 2020
4-6 hours/week
Sample-based Learning Methods
University of Alberta & Alberta Machine Intelligence Institute
Taught by: Martha White & Adam White
Completed by: Sung-yeon LIM by April 30, 2020
4-6 hours/week
Prediction and Control with Function Approximation
University of Alberta & Alberta Machine Intelligence Institute
Taught by: Martha White & Adam White
Completed by: Sung-yeon LIM by August 24, 2020
4-6 hours/week
A Complete Reinforcement Learning System (Capstone)
University of Alberta & Alberta Machine Intelligence Institute
Taught by: Martha White & Adam White
Completed by: Sung-yeon LIM by August 30, 2020
4-6 hours/week