In this compilation of machine learning exercises by Michael Gutmann, various pen-and-paper activities cover topics such as linear algebra, optimization, graphical models, hidden Markov models, and more. The collection delves into model-based learning, sampling techniques, and variational inference. This resource provides a hands-on approach to understanding complex machine learning concepts. The exercises offer a practical way to enhance knowledge in the field. Unique in its focus on pen-and-paper activities, this compilation offers a different perspective on machine learning practice. Please note that some might find the lack of practical implementation controversial.
https://arxiv.org/abs/2206.13446