An introduction about how to train computers to interact wisely with a stochastic process scenario to maximize certain benefit/reward
Duration
8 to 12 h
Syllabus
Markov decision processes, dynamic programming and related algorithms
Fundamental concepts in Reinforcement Learning:
- temporal difference methods,
- approximate dynamic programming
- deep reinforcement learning
Prerequisites
“Foundations of Data Science”, highly advisable “Deep Learning and applications”
Credential
Part of ‘Stochastic models and optimization’ in MSc in Data Science, also taught in Data Science Center workshops