Teaching

Advanced course covering cutting-edge topics in reinforcement learning and the design of autonomous AI agents capable of long-term planning and decision-making in complex environments.

Foundational course introducing deep reinforcement learning algorithms, combining neural networks with RL. Topics include DQN, Policy Gradients, Actor-Critic methods, and practical implementations.

Comprehensive introduction to reinforcement learning covering Markov Decision Processes, value-based methods (Q-learning, SARSA), policy-based methods, and exploration-exploitation trade-offs.

Advanced seminar covering recent research in RL including model-based RL, meta-learning, multi-agent systems, safe RL, and applications in robotics and control.

Course exploring specialized topics in data science including time series analysis, Bayesian methods, uncertainty quantification, and applications in industrial systems.

Interdisciplinary course exploring mathematical and statistical methods for personalized medicine, including dynamic treatment regimes and reinforcement learning in healthcare.

Seminar for undergraduate students covering current topics in artificial intelligence, providing an introduction to AI research methods and paper presentation skills.