Lectures

Tensorization and uncertainty quantification in deep learning.

Including Markov-Chain Monte-Carlo methods and Variational Inference on Bayesian Neural Networks.

Fundamental topics such as MLE, optimization and a brief history of NN.

Variational Autoencoders and Generative Adversarial Nets.