Pen and Paper Exercises in Machine Learning
Machine Learning
2022-06-28 v1 Machine Learning
Abstract
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.
Keywords
Cite
@article{arxiv.2206.13446,
title = {Pen and Paper Exercises in Machine Learning},
author = {Michael U. Gutmann},
journal= {arXiv preprint arXiv:2206.13446},
year = {2022}
}
Comments
The associated github page is https://github.com/michaelgutmann/ml-pen-and-paper-exercises