Pen and Paper Exercises in Machine Learning
This is an incremental educational resource for students and practitioners in machine learning to reinforce theoretical concepts.
The paper presents a collection of pen-and-paper exercises covering fundamental machine learning topics such as linear algebra, optimization, graphical models, and inference methods, aimed at educational purposes without reporting specific results or numbers.
This is a collection of (mostly) pen-and-paper exercises in machine learning. The exercises are on the following topics: linear algebra, optimisation, directed graphical models, undirected graphical models, expressive power of graphical models, factor graphs and message passing, inference for hidden Markov models, model-based learning (including ICA and unnormalised models), sampling and Monte-Carlo integration, and variational inference.