Covers information measures like entropy, Kullback-Leibler divergence, and data processing inequality, along with probability kernels and mutual information.
Covers optimization techniques in machine learning, focusing on convexity, algorithms, and their applications in ensuring efficient convergence to global minima.
Explores the concept of entropy as the average number of questions needed to guess a randomly chosen letter in a sequence, emphasizing its enduring relevance in information theory.
