Fundamentals of Inference and Learning

This lecture introduces the course on the theory of statistics, inference, and machine learning, focusing on theoretical understanding and practical exercises in Python. Topics include statistical and Bayesian inference, supervised and unsupervised learning, statistical learning theory, deep learning, and basics of generative models and reinforcement learning. The course structure involves a combination of mathematical foundations and practical computational aspects, with exercises and projects contributing significantly to the final grade.