Artificial Intelligence for the Humanities 

Artificial Intelligence is a popular and growing trend that is affecting many areas of life and academic disciplines, including the humanities and social sciences. In response to the growing importance of rapidly emerging computational technologies, this course presents the technological foundations of Artificial Intelligence in an accessible manner. It is intended for undergraduate and graduate students in the Humanities and Social Sciences.

Course objectives

During the course, students will acquire the theoretical knowledge and practical skills necessary for solving real-world problems using available data and Artificial Intelligence methods, particularly in text analysis. To this end, students will learn to use tools in the R programming system and independently navigate technical literature. Graduates of the course will be able to analyze and process data from various Humanities and Social Science disciplines and use it for experiments with Artificial Intelligence.

Prerequisites

Students should be motivated to experiment with Artificial Intelligence, including neural networks. They are expected to have basic proficiency in the R system as well as elementary knowledge of systematic data processing and statistical analysis.

Schedule 2025/26

The classes will take place at the Jinonice Campus (U Kříže 661, Prague 5) in the room TBA, always on Fridays.

• lectures TBA
• lab session TBA

R system

• Recommended introductory course in working with R: Cultural Analytics: Interactive Learning Environment in R (Lessons 1-4)

Literature

• Arnold, Taylor and Lauren Tilton: Humanities Data in R. Exploring Networks, Geospatial Data, Images, and Text. Springer, 2015. [https://link.springer.com/book/10.1007/978-3-319-20702-5]
• Lantz, Brett: Machine Learning with R. PACKT Publishing. 2013, 2019. [https://www.packtpub.com/product/machine-learning-with-r-third-edition/9781788295864]
• Grolemund, Garrett and Hadley Wickham: R for Data Science: Import, Tidy, Transform, Visualize, and Model Data. O'Reilly Media, 2016. [https://r4ds.hadley.nz/]
• Boehmke, Bradley and Brandon M. Greenwell: Hands-On Machine Learning with R. Chapman & Hall / CRC, 2019. [https://bradleyboehmke.github.io/HOML/]
• Hvitfeldt, Emil and Julie Silge: Supervised Machine Learning for Text Analysis in R. CRC Press. 2022. [SMLTAR | https://smltar.com/]

Other sources

• Chollet François, Tomasz Kalinowski, and J. J. Allaire. Deep Learning with R. Manning Publications, Second Edition, 2022. [DLR | https://www.manning.com/books/deep-learning-with-r-second-edition]
• Martinková Patricia, Hladká Adéla. Computational Aspects of Psychometric Methods With R. Chapman & Hall, 2023. [PAT | doi]