Lectures related to Vapnik-Chervonenkis dimension

Covers the Fourier inversion formula, exploring its mathematical concepts and applications, emphasizing the importance of understanding the sign.

Offers insights into the statistical physics of learning, exploring the relationship between neural network structure and disordered systems.

Explores the challenges and insights of deep learning, focusing on loss landscape, generalization, and feature learning.

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

Explains compression and Kraft inequality in codes and sequences.

Explores linear models for classification, including logistic regression, decision boundaries, and support vector machines.