Foundations of digital humanities

This course gives an introduction to the fundamental concepts and methods of the Digital Humanities, both from a theoretical and applied point of view. The course introduces the Digital Humanities circle of processing and interpretation, from data acquisition to new understandings.  The first part of the course presents the technical pipelines for digitising, analysing and modelling written documents (printed and handwritten), maps, photographs and 3d objects and environments. The second part of the course details the principles of the most important algorithms for document processing (layout analysis, deep learning methods), knowledge modelling (semantic web, ontologies, graph databases) generative models and simulation (rule-based inference, deep learning based generation). The third part of the course focuses on platform management from the points of view of data, users and bots. In each part, ethical and societal issues are discussed. Students practice the skills they learn directly analysing and interpreting Cultural Datasets from ongoing large-scale research projects conducted at EPFL.

Week 1

Introduction to the course and Digital Humanities, structure of the course

Week 2

Introduction to the DH circle of processing and interpretation (acquisition, processing, analysis, visualisation, UX, interpretation). From data acquisition to new understandings.

Part I : Pipelines

Week 3

Pipeline for Written documents (Printed and Handwritten). Transcription, Named Entities, Semantic modelling,Topic and Document modelling.

Week 4

Pipeline for Maps. Vectorization. Alignment. Homologs Points.

Week 5

Pipeline for Artworks photographs. Segmentation. Features detection. Detail search.

Week 6

Pipeline for 3D spaces. Photogrammety. Diachronic realignment.

Part II : Algorithms

Week 7

Algorithms for Document processing : Document analysis and Deep learning methods

Week 8

Algorithms for Knowledge modelling : Semantic web, ontologies, graph database, homologous points, disambiguation.

Week 9

Algorithms for Generative models and simulation : Rule-based inference, Deep learning based generation

Part III : Platform management

Week 10

Data Management  : Computing infrastructure,  Data Management models, Sustainability. Apps. Example of Wikipedia and Europeana.

Week 11

User Management : Representation, Rights, Traceability, Vandalism, Motivation, Negotiation spaces

Week 12

Bot Management : Versioning. Open source repositories.