Lecture

Fundamentals of statistics and probability theory for data analysis, taught as an inverted classroom with ALA, an AI chatbot for practising the material.

A broad introduction to reinforcement learning: Markov decision processes, Monte-Carlo and temporal difference methods, and deep reinforcement learning, with agents trained in Gymnasium.

Fundamentals of statistics and probability theory for data analysis: descriptive statistics, random variables, parameter estimation, statistical testing, Monte Carlo methods and stochastic processes.

A broad introduction to reinforcement learning: Markov decision processes, Monte-Carlo and temporal difference methods, and deep reinforcement learning, with agents trained in Gymnasium.