INTRODUCTION TO REINFORCEMENT LEARNING

Description

This course explores fundamental concepts, algorithms, and applications in reinforcement learning, bridging AI and machine learning. Through lectures and practical exercises, students study Markov Decision Processes, Bellman equations, and key algorithms such as Q-learning, and DQN. They discover Reinforcement Learning’s road applicability in robotics, gaming, finance, and healthcare domains.

Prerequisites

Data Science 3000A/B .

Reviews

Write a review