Title: Reinforcement Learning Through a Probabilistic Lens

Abstract: This talk introduces machine learning fundamentals through their probabilistic foundations, emphasizing connections between maximum likelihood estimation, loss functions, and neural networks. We will then explore core principles of reinforcement learning: from Markov Decision Processes and value functions to algorithms like Q-learning, DQN, and PPO. The presentation concludes with recent developments of RL to large language models and preference optimization.