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https://github.com/michaelgutmann/ml-pen-and-paper-exercises
Pen and paper exercises in machine learning
https://github.com/michaelgutmann/ml-pen-and-paper-exercises
exercises-solutions machine-learning mathematics
Last synced: 12 days ago
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Pen and paper exercises in machine learning
- Host: GitHub
- URL: https://github.com/michaelgutmann/ml-pen-and-paper-exercises
- Owner: michaelgutmann
- Created: 2022-06-27T16:33:05.000Z (over 2 years ago)
- Default Branch: master
- Last Pushed: 2024-05-21T20:05:42.000Z (6 months ago)
- Last Synced: 2024-08-01T16:38:56.059Z (3 months ago)
- Topics: exercises-solutions, machine-learning, mathematics
- Language: TeX
- Homepage:
- Size: 1.73 MB
- Stars: 1,811
- Watchers: 30
- Forks: 136
- Open Issues: 3
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
README
# Pen and paper exercises in machine learning
[![CC BY 4.0][cc-by-shield]][cc-by]
This is a collection of (mostly) pen-and-paper exercises in machine learning.
Each exercise comes with a detailed solution. The following topics are covered:
- 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
- variational inference
A compiled pdf is available on [arXiv](https://arxiv.org/abs/2206.13446).
Please use the following reference for citations:
```
@TechReport{Gutmann2022a,
author = {Michael U. Gutmann},
title = {Pen and Paper Exercises in Machine Learning},
institution = {University of Edinburgh},
year = {2022},
arxiv = {https://arxiv.org/abs/2206.13446},
url = {https://github.com/michaelgutmann/ml-pen-and-paper-exercises},
}
```
The work is licensed under a
[Creative Commons Attribution 4.0 International License][cc-by].
[cc-by]: http://creativecommons.org/licenses/by/4.0/
[cc-by-shield]: https://img.shields.io/badge/License-CC%20BY%204.0-lightgrey.svg
## Usage
Under linux, you can compile the collection with `make`. To remove temporary files, use `make clean`.
By default, the compiled document includes the solutions for the exercises. To
compile a document without the solutions, comment `\SOLtrue` and uncomment
`\SOLfalse` in `main.tex`.
## Contributing
Please use GitHub's issues to report mistakes or typos. I would welcome
community contributions. The main idea is to provide exercises together with
_detailed_ solutions. Please get in touch to discuss options. My contact
information is available [here](https://michaelgutmann.github.io/).
## Acknowledgements
The tikz settings are based on macros kindly shared by [David
Barber](http://web4.cs.ucl.ac.uk/staff/D.Barber/pmwiki/pmwiki.php?n=Main.HomePage).
The macros were partly used for his book [Bayesian Reasoning and Machine
Learning](http://web4.cs.ucl.ac.uk/staff/D.Barber/pmwiki/pmwiki.php?n=Brml.HomePage).
I make use of the [`ethuebung` package](https://github.com/phfaist/ethuebung)
developed by Philippe Faist. I hacked the style file to support multiple
chapters and inclusion of the exercises in a table of contents. I developed
parts of the linear algebra and optimisation exercises for the course
[Unsupervised Machine
Learning](https://www.cs.helsinki.fi/u/ahyvarin/teaching/uml2009/) at the
University of Helsinki and the remaining exercises for the course [Probabilistic
Modelling and Reasoning](http://www.inf.ed.ac.uk/teaching/courses/pmr/21-22) at
the University of Edinburgh.