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Last update: PhDr. Jiří Kocián, Ph.D. (05.02.2024)
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Last update: Mgr. Klára Kosová (02.02.2024)
Students will acquire fundamental knowledge, skills, and orientation in Digital Humanities. They become familiar with the most important concepts, operations, and subfields of DH. This course serves as an introductory class for the certified program in AI in Digital Humanities and is therefore directly connected to the other parallel and following courses aimed at a detailed understanding of ML (NPFL 112, NPFL 142, NPFL 143).
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Last update: Mgr. Klára Kosová (02.02.2024)
compulsory attendance minimum 50% points in part B) and C) each A: 100-91 pts B: 90-81 pts C: 80-71 pts D: 70-61 pts E: 60-51 pts F(failed): 50 pts or less
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Last update: PhDr. Jiří Kocián, Ph.D. (05.02.2024)
Recommended reading Accelerating Social and Behavioral Science Through Ontology Development and Use | National Academies (n.d.). Available at: https://www.nationalacademies.org/our-work/accelerating-social-and-behavioral-science-through-ontology-development-and-use (accessed 9 October 2023). Arnold T and Tilton L (2015) Humanities Data in R: Exploring Networks, Geospatial Data, Images, and Text. Quantitative Methods in the Humanities and Social Sciences. Cham: Springer International Publishing. Available at: https://link.springer.com/10.1007/978-3-319-20702-5 (accessed 9 October 2023). Greenwell BB& B (n.d.) Hands-On Machine Learning with R. Available at: https://bradleyboehmke.github.io/HOML/ (accessed 9 October 2023). Krippendorff KH (2018) Content Analysis: An Introduction to Its Methodology. Fourth edition. Los Angeles: SAGE Publications, Inc. Piotrowski M (2012) Natural Language Processing for Historical Texts. Synthesis Lectures on Human Language Technologies. Cham: Springer International Publishing. Available at: https://link.springer.com/10.1007/978-3-031-02146-6 (accessed 9 October 2023). R for Data Science (2e) (n.d.). Available at: https://r4ds.hadley.nz/ (accessed 9 October 2023). Ramírez AG, Mejía JM, Martin PV, et al. (2023) Digital Humanities, Corpus and Language Technology / Humanidades Digitales, Corpus y Tecnología Del Lenguaje. University of Groningen Press. Available at: https://books.ugp.rug.nl/index.php/ugp/catalog/book/128 (accessed 1 February 2024). Silge EH and J (n.d.) Supervised Machine Learning for Text Analysis in R. Available at: https://smltar.com/ (accessed 9 October 2023). |
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Last update: Mgr. Klára Kosová (02.02.2024)
This is a bloc course with six sessions every two weeks of the semester, physical presence is required.
Students complete individual tasks after each session and collaborate on a group project to produce a salient research design proposal by the end of the semester.
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Last update: PhDr. Jiří Kocián, Ph.D. (05.02.2024)
The final grade (100 points) comprises fulfilling three partial activities A) active participation in classes (10 pts) B) regular individual homework assignments (40 pts.) C) groupwork research design (50pts) |
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Last update: PhDr. Jiří Kocián, Ph.D. (05.02.2024)
The course program is organized into six teaching sessions, each comprising two standard-length classes. Each session represents one consistent thematic/methodological bloc, split into conceptual parts, practical training, and an introduction to basic programming concepts. Each class is accompanied by a compulsory reading of (one or two journal articles) to represent best practices in research application and leads into an individual homework assignment for the off week. 1. Introduction to fundamentals of computational science and computer operation 2. From sources to data - working with text and XML 3. Processing images and sound A. Midterm research design seminar 4. Databases and SQL filtering 5. Qualitative coding as the basis of text analysis 6. GIS - Geographic information systems Object-oriented programming B. Final research design presentation |
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Last update: Mgr. Klára Kosová (02.02.2024)
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