Lectures related to VC dimension

Offers a crash course on statistical learning theory, covering PAC-learning and loss functions.

Explores nonuniform learnability, PAC theorems, and structural risk minimization in machine learning.

Explores growth rate, uniform convergence, PAC learning, and distribution learning challenges.

Explores the intersection between machine learning and cryptography, focusing on safe machine learning through cryptographic tools and models.

Introduces the basics of machine learning, covering supervised learning, reinforcement learning, and dimension reduction.

Explores uniform convergence, loss functions, and the No-Free-Lunch theorem in machine learning.