Academic year 2020 2021

During this academic year, I teach the following courses (in chronological order):

  • Introduction to digital humanities (BA, with Rens Bod)

The Digital Humanities are increasingly relevant for scholars in all Humanities disciplines. The rapidly rising digital availability of content, as well as the broader digitization of society, offer new exciting opportunities to humanists. This course serves as an introduction to the Digital Humanities (DH) as a research area and community.

  • AI for society (BSc, with Tobias Blanke)

AI is rapidly becoming part of our daily lives in many visible and invisible ways. Consequently, the social sciences and humanities are increasingly contributing to dominant research problems in AI. Examples include the ethical profiling of AI systems, including their privacy and governance, questions of fairness and accountability, as well as data bias and model interpretability. This course provides an opportunity to engage with these increasingly mainstream research questions. It does so by proposing a pathway into the main critical themes emerging when AI is applied in real-world scenarios, and it does so by highlighting the bi-directional contributions of the social sciences and humanities.

  • Text mining (BSc)

The increasing amount of digitally available text data contains a wealth of information about topics, people, products, behaviours. Due to its numerous applications (research, industry, government, non-profit, etc), extracting information from, and working with, texts is of crucial importance. This is the aim of text mining. This course provides an introduction to text mining and natural language processing. The course will introduce fundamental concepts and techniques from (computational) linguistics, which are used to represent and model texts mathematically. It will also develop on machine learning techniques that allow us to make inferences and predictions about, e.g., user experiences, marketing and human behaviour from data that are generated and collected online.

  • Foundations of social and cultural data analysis (MA)

In this course, you will learn how to code in Python and analyze data on socio-cultural phenomena. Socio-cultural data pose great challenges and offer many opportunities due to their variety and richness. Examples include historical and literary sources, social networks and media. The course offers a pathway into applied data analysis concepts and techniques, with applications in the arts and humanists

  • Applications of social and cultural data analysis (MA)

In this course, you will practice data analysis applied to socio-cultural phenomena on a real-world research project of their choice. During this course, students will exclusively focus on an individual or group project, possibly as part of a larger research project, giving them ample opportunity to explore a research question, dataset, and technique(s). Students will be particularly encouraged to work on a topic of their choice.

  • Digital humanities lab (BA)

In this laboratory course, students engage with a Digital Humanities project. Students will be able to work on a real-world research project in collaboration with a team or work independently on a topic of choice. A list of projects will be provided, but the students are encouraged to propose their own. Students can work individually or in small groups (of up to 3). The students will be encouraged to cross the boundaries between previous courses, and to relate their project results within the broader context of the program.