Covers the principles and methods of clustering in machine learning, including similarity measures, PCA projection, K-means, and initialization impact.
Delves into deep learning's dimensionality, data representation, and performance in classifying large-dimensional data, exploring the curse of dimensionality and the neural tangent kernel.