Teaching

This course provides a comprehensive introduction to Reinforcement Learning (RL), offering students an in-depth exploration of the field’s core challenges and methodologies. Throughout the course, students will delve into the foundational concepts and various approaches within RL. By the end of the course, they will have acquired the essential knowledge and skills necessary to excel in the field of Reinforcement Learning.

Learning Outcomes:

Upon successful completion of the course, students will:

• Understand Key Concepts: Clearly define the distinguishing features of Reinforcement Learning and differentiate it from other areas of Machine Learning.
• Practical Implementation: Develop the ability to implement standard RL algorithms in code, gaining hands-on experience in practical applications.
• Critical Analysis: Evaluate RL algorithms based on criteria such as regret, sample complexity, computational complexity, empirical performance, and convergence. 
• Problem-Solving: Formulate and solve complex sequential decision-making problems using appropriate RL tools and techniques.

More information can be found at: Reinforcement Learning