- Machine Learning
- Algorithms
- Human Learning
- Machine Learning Algorithms
- Applied Machine Learning
- Computer Programming
- Python Programming
- Artificial Neural Networks
- Decision Making
- Mathematics
- Probability & Statistics
- Statistical Programming
July 5, 2021
Approximately 2 months at 10 hours a week to completeStelios Andrew Stavroulakis'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)
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
Earned after completing each course in the Specialization
University of Alberta & Alberta Machine Intelligence Institute
Taught by: Martha White & Adam White
Completed by: Stelios Andrew Stavroulakis by May 11, 2020
4-6 hours/week
University of Alberta & Alberta Machine Intelligence Institute
Taught by: Martha White & Adam White
Completed by: Stelios Andrew Stavroulakis by June 29, 2020
4-6 hours/week
University of Alberta & Alberta Machine Intelligence Institute
Taught by: Martha White & Adam White
Completed by: Stelios Andrew Stavroulakis by July 4, 2021
4-6 hours/week
University of Alberta & Alberta Machine Intelligence Institute
Taught by: Martha White & Adam White
Completed by: Stelios Andrew Stavroulakis by July 5, 2021
4-6 hours/week