Artificial Intelligence (AI) is a branch of computer science aimed at developing computer systems that can solve complex problems by simulating human cognitive functions
This tutorial provides interdisciplinary insight into the challenges stemming from the rapid development in and implementation of Artificial Intelligence. Learners will be equipped with a broader understanding of the philosophical issues surrounding AI as well as current real-world examples which are developing our relationship to AI and its growth.
The foundational skills in Distant Viewing and computer vision are increasingly relevant in the Digital Humanities, yet educational resources are often aimed at those with a background in computer science and statistics. For example, machine learning and digital image processing are fundamental to the Computational Humanities, but many scholars in the Digital Humanities lack accessible training in these areas.
The goal of this project was to create a focused course enabling students to acquire essential skills in computer vision, specifically tailored for Digital Humanists. Upon completing this course, students will possess a foundational understanding of digital image representation, computer vision methodologies, and machine learning techniques, all contextualized within a Digital Humanities framework.
This class is part of the project "Computer Vision for Digital Humanists" and licensed Creative Commons BY NC SA. This project (2023) was funded by CLARIAH-AT with the support of BMBWF. It was made possible by major contributions from the ERC DiDip project (From Digital to Distant Diplomatics). The video was produced by Moving Stills.
The goal of the project "Computer Vision for Digital Humanists" was the creation of educational self-learning resources on Computer Vision specifically for Digital Humanities, consisting of slide decks, Jupyter Notebooks with practical exercises in Python as well as teaching videos ( see the YouTube playlist. They cover a range of topics from the basics of computer vision and machine learning to training custom deep learning models for one's own historical data.
