2026/27 autumn semester digital humanities courses in English

Student will acquire the basics of algorithmic thinking and get acquainted with some of the programming techniques.

After passing the course, the student:
- is motivated to use computers and to develop necessary programs for further studies;
- can demonstrate basic programming constructs (branching, loops, subprogram) as programming sections;
- can develop algorithms for simple text-based problems.

Brief description of content:
Algorithms and programs. Representations of algorithms, flow-charts. Branching algorithms. Loops. Sub-algorithms. Developing algorithms for given text-based problems. Program structure. Names. Variables. Operations. Expressions. Boolean expressions, comparisons. Conditional statements. Loop statements. Lists. Functions. User input. Reading from a file. Writing to a file. Simple user interface.

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Digital environment rapidly changed the traditional framework in the studies of culture and literature. It could be new ways of storing and representing cultural data or understanding the nature of a digital document; an automated text analysis or computer modeling of complex cultural systems under the emerging data-sensitive theories (as the theory of cultural evolution). No doubt the "digital" became a new norm with blurry borders & elusive definition.
The course is planned to provide students with a roadmap in this essentially trans-disciplinary world, that jumps between editorial theories, geographic information systems, computational linguistics, media studies and network science. Far from being exhaustive, this course will overview major fields, approaches and ideas in DH while paying slightly more attention to historical cultural data & text analysis.

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The aim of the course is to introduce the theoretical and methodological framework of computational literary studies (CLS). After covering fundamental concepts such as close and distant reading, and the relationship between CLS, data science, and computational linguistics, the course will examine research that has produced significant results from a literary studies perspective. Topics will include long-term trends in literary history, genre theory, stylistics, and the relationship between changes in literary form and textual content. The course will primarily consist of reading and discussing key studies, complemented by presentations on ongoing research projects. The aim of the course is to provide a broad overview of the diverse research areas of computational literary studies, to familiarize students with the range of research questions and methodologies in the field, and to inspire them to formulate their own questions.

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The course introduces fundamental concepts of contemporary AI, and discusses state-of-the-art Large Language Models (LLM) and their typical (but also less typical) applications. While the course will partly involve entry-level coding in Python (by using relatively simple scripts), no programing skills will be necessary to follow the course. The course is devided into two parts:

The first half of the course covers fundamental concepts if contemporary AI, ranging from the notion of predictive modeling, all the way to state-of-the-art LLM systems based on attention mechanisms. The second half of the course will be focused on actual issues of contemporary AI, including prompt engineering, setting up a local LLM instance, fine-tuning a model, and using retrieval-augmented generation (RAG) to mitigate the problem of hallucinations.

Lectures will provide an introduction to AI models on a conceptual level, without discussing in depth the mathematical details of these modes. In particular, no proficiency in linear algebra will be requred. However, the students will receive an understanding of how these models work, what the attention mechanism is, and how they "understand" natural language. The hands-on seminars will provide an introduction to inference with LLMs, fine-tuning the models, and using them in different humanities contexts.

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