Machine Learning for the Arts&Humanities

This repository is part of the course Machine Learning for the Arts&Humanities at the University of Bologna, master degree in Digital Humanities and Digital Knowledge.

Please note that this repository will be updated continuosly in the future, as new editions of this course are proposed.

Contents

1. Linear regression
2. Linear classification
3. PyTorch
4. Machine Vision
5. Language Processing
6. Retrieval Augmented Generation

Datasets and exercises

We will use several datasets, available in the Data folder.

These datasets include:

• Applied Data Analysis, various datasets.
• Computer Vision for the Humanities: An Introduction to Deep Learning for Image Classification from the Programming Historian series.
• British Library 19th century books.
• TopRes19th dataset.

Setting-up your working environment

Please see the requirements file for a list of dependencies. PyTorch can be installed following these instructions. Lastly, to setup your working environment, refer to this guide.

Book

These materials are in part based on the book Dive into Deep Learning.

Acknowledgements

Some materials are re-purposed from:

• UvA Deep Learning course.
• PyTorch tutorials.