AI Academy

Fundamentals of Reinforcement Learning

Instructor: Martha White , Adam White Duration: Approx. 15 hours
Objective 1 Formalize problems as Markov Decision Processes
Objective 2 Understand basic exploration methods and the exploration / exploitation tradeoff
Objective 3 Understand value functions, as a general-purpose tool for optimal decision-making
Objective 4 Know how to implement dynamic programming as an efficient solution approach to an industrial control problem
Markov Model
Algorithms
Machine Learning
Reinforcement Learning
Artificial Intelligence
Probability Distribution
Machine Learning Algorithms

Sample-based Learning Methods

Instructor: Martha White , Adam White Duration: 2 weeks at 10 hours a week
Algorithms
Simulations
Artificial Intelligence and Machine Learning (AI/ML)
Probability Distribution
Machine Learning Algorithms
Reinforcement Learning
Machine Learning
Sampling (Statistics)

Prediction and Control with Function Approximation

Instructor: Martha White , Adam White Duration: Approx. 21 hours
Artificial Neural Networks
Probability Distribution
Machine Learning
Feature Engineering
Reinforcement Learning
Supervised Learning
Pseudocode
Deep Learning
Linear Algebra

A Complete Reinforcement Learning System (Capstone)

Instructor: Martha White , Adam White Duration: Approx. 15 hours
Artificial Neural Networks
Machine Learning Algorithms
Markov Model
Algorithms
Artificial Intelligence
Performance Testing
Reinforcement Learning
Debugging
Solution Architecture