Fundamentals of AI and Machine Learning
for Byzantine Studies II
Postgraduate Programme — Level 7
LEARNING OUTCOMES
Upon completion, students will be able to:
1)
Select appropriate AI-assisted methods for different kinds of Byzantine research questions while recognizing when a method is unsuitable.
2)
Use simple workflows to explore textual, visual, spatial, and structured humanities data.
3)
Compare the outputs of different AI tools and document differences in reliability, coverage, bias, and failure modes.
4)
Apply basic principles of prompt design, source verification, and citation checking when using generative AI for research support tasks.
5)
Understand the foundational logic of text analysis, image analysis, spatial analysis, network thinking, and knowledge representation without duplicating the specialized technical courses.
6)
Design a transparent AI-assisted workflow including data source, method choice, tool use, verification protocol, ethical considerations, and limitations.
7)
Communicate AI-assisted research results responsibly to academic and non-specialist audiences, distinguishing evidence from interpretation and speculation.
8)
Reflect critically on legal, ethical, cultural, and epistemological issues in AI-assisted Byzantine Studies.
COURSE SYLLABUS
13 Modules
Week 01 | From AI concepts to research workflows
Recap of Course I; the research workflow from question formulation to evidence, method, verification, interpretation, and communication.
Week 02 | Choosing methods responsibly
Matching research questions to data types and methods. Tool suitability, data availability, pilot testing, negative results, and when not to use AI.
Week 03 | Prompting as research support, not evidence
Prompt design for summaries, checklists, translation triage, comparison, and brainstorming. Verification requirements and documentation of AI assistance.
Week 04 | Text analysis at a foundational level
What text analysis can reveal: frequency, keywords, similarity, and entity extraction as concepts. Brief demonstrations with prepared examples; detailed NLP implementation is reserved for the dedicated NLP course.
Week 05 | Visual AI at a foundational level
Images as data; classification, detection, similarity, and restoration as concepts. Byzantine icons, seals, coins, manuscripts, and mosaics as examples; detailed 3D/MR practice is reserved for the Mixed Reality course.
Week 06 | Spatial and temporal thinking at a foundational level
What GIS and timelines can contribute to Byzantine Studies. Places, uncertainty, movement, territories, and change over time. Full QGIS practice is reserved for the GIS course.
Week 07 | Relationships, networks, and structured knowledge
Persons, places, offices, texts, events, and objects as linked entities. Introductory network and knowledge-graph thinking without formal modelling requirements.
Week 08 | Evaluation beyond metrics
Qualitative and quantitative evaluation of AI-assisted workflows. Error typologies, false confidence, bias, missing data, domain mismatch, and scholarly usefulness.
Week 09 | Reproducibility and documentation
Workflow documentation, versioning, data dictionaries, tool logs, model/tool version notes, rights statements, and transparent reporting of uncertainty.
Week 10 | Ethics, law, and academic integrity
Copyright, digitized sources, privacy, cultural sensitivity, hallucinated references, AI attribution, publication policies, and responsible use in teaching and research.
Week 11 | Communicating AI-assisted research
How to present AI-supported findings in papers, posters, presentations, digital outputs, and public-facing cultural heritage contexts without overclaiming.
Week 12 | Capstone workshop
Students design or refine an AI-assisted Byzantine Studies workflow. Peer and instructor feedback focuses on feasibility, evidence, ethics, and verification.
Week 13 | Final presentations and programme integration
Presentation of capstone workflows and reflection on how students may proceed into specialized electives or thesis work.
ASSESSMENT
Student Evaluation
40%
Writtent Assignments
40%
Final Project
20%
Workload — ECTS Distribution
250 Hours Total
Lectures
39
Guided exercises / workshops
30
40
55
40
26
20
Course Total
250
Recommended Bibliography
Suggested bibliography:
- Mitchell, M. Artificial Intelligence: A Guide for Thinking Humans. Farrar, Straus and Giroux, 2019.
- Karsdorp, F., Kestemont, M., and Riddell, A. Humanities Data Analysis: Case Studies with Python. Princeton University Press, 2021.
- Moretti, F. Distant Reading. Verso, 2013.
- Burdick, A., Drucker, J., Lunenfeld, P., Presner, T., and Schnapp, J. Digital_Humanities. MIT Press, 2012.
- Drucker, J. The Digital Humanities Coursebook. Routledge, 2021.
- Baca, M. (ed.). Introduction to Metadata. Getty Research Institute, selected chapters.
- The Programming Historian. Selected beginner-friendly lessons on data literacy, OCR, text analysis, and digital humanities methods.
- European Commission. Ethics Guidelines for Trustworthy AI, 2019.
- UNESCO. Recommendation on the Ethics of Artificial Intelligence, 2021.
Related academic journals:
- International Journal of Digital Humanities (Springer)
- Journal on Computing and Cultural Heritage (ACM)
- Digital Scholarship in the Humanities (Oxford University Press)
- Journal of Open Humanities Data
- Journal of Cultural Heritage