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06.11 (2h) A panorama of Deep learning methods. Successes. Fundamental principles. Neurons. Receptive Fields. Hierarchical representation / texture. Gradient descent. Credit Assignment Path. Most important architectures. Convolutional neural networks. Recurrent neural networks. Siamese Networks. Word2Vec. Generative Adversarial Networks. Style Transfer. Importance of Deep learning for Digital Humanities. Can Deep Learning networks and Big Data of the Past lead to new forms of Artificial Intelligence ?  
06.11 (2h) A panorama of Deep learning methods. Successes. Fundamental principles. Neurons. Receptive Fields. Hierarchical representation / texture. Gradient descent. Credit Assignment Path. Most important architectures. Convolutional neural networks. Recurrent neural networks. Siamese Networks. Word2Vec. Generative Adversarial Networks. Style Transfer. Importance of Deep learning for Digital Humanities. Can Deep Learning networks and Big Data of the Past lead to new forms of Artificial Intelligence ?  


07.11 (2h) Computer vision and deep learning tutorial (Sofia). Conditional Random Fields (CRF) tutorial.
07.11 (2h) Computer vision and deep learning tutorial (Sofia). Conditional Random Fields (CRF) tutorial. Both are available on [https://github.com/dhlab-epfl/fdh-tutorials this Github repository].


==== Week 9 : Project  ====
==== Week 9 : Project  ====

Revision as of 08:03, 7 November 2019

Welcome to the wiki of the course Foundation of Digital Humanities (DH-405).

Contact

Professor: Frédéric Kaplan

Assistant: Raphaël Barman

Rooms: Wednesday (CM1113) and Thursday (BC03)

Links

Summary

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 and services. 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 in particular deep learning approaches (for document analysis and image generation) and knowledge modelling (semantic web, ontologies, graph databases). The third part of the course focuses on platform management from the points of view of data, users and bots. Students will practise the skills they learn by engaging in a class-wide collective project.

Plan

Introduction

Week 1 : Structural tensions in Digital Humanities

18.09 (2h) Introduction to the course and Digital Humanities, structure of the course. Introduction to Framapad with a simple exercise. Principle of collective note talking and use in the course. State of the Digital Humanities at EPFL, in Switzerland and in Europe. Structuring tensions 1: Digital Humanities, Digital Studies, Humanities Computing and Studies about Digital Culture. Digital Humanism vs. Digital Humanities. Why digital methods tend to dissolve traditional disciplinary frontiers. A focus on practice. Translation issues.

20.09 (2h) (a) Structuring tensions 2: Big Data Digital Humanities vs Small Data Digital Humanities. The 3 circles. Exercise on relationship between elements in Digital Culture schema. (2h) Practical session: Introduction to MediaWiki. Objective: Learning the basic syntax of MediaWiki. Get a first experience of collaborative editing. Learning to write from a neutral point of view. Creation of a Wikipedia page

Week 2 : The Digital Humanities Circle and Digitization processes

25.09 (1h) Introduction to the DH circle linking the digitisation of sources, their processing, their analysis, visualisation and the creation of societal value (insight, culture) leading ultimately to the digitisation of new sources. Presentation of some sustainable DH circles (genealogy, image banks). Patrimonial capitalism and the risk of monopolistic companies. Parallelism with the race for sequencing the Human Genome. Introduction to the TIme Machine and mutualised infrastructure approach. (1h) Introduction to the the Digitization Process. Document digitisation as a problem of conversion of dimensions. Digitisation is logistic optimization. Alienation. Digitisation on demand.


26.09 (2h) Introduction to projects. Presentation of last year projects. This years's focus : Paris. Alignement of the street network and addresses. Context: BnF / INHA. Presentation of example of research on Paris directories. (2h) General Gallica presentation. Pre-recorded queries. General presentation of the Archives de Paris website. Special collections. Formation of the pairs. Global Project sketching (from Interfaces to pipelines and data sources)

Part I : Pipelines

Week 3: Written Documents (2D) pipeline

02.10 (2h) Sketch consolidation. One sketch per project (including text and drawing of the interface). Each group must do at least 2 project sketches. Fill in the table in the Projects page by tomorrow.

03.10 (2h) Sketches presentations. (2h) Pipeline for Written documents. Part I: Standards. Open Annotation Data Model. Shared Canvas. Part 2: Regulated Representations, Homologous points and the construction of hypothetical realities.

Week 4: Projects

09.10 (2h) Presentation of the Gallica wrapper.

10.10 (4h) Presentation of BNF XML ALTO and PAGE XML, Transkribus and VGG Image annotator (VIA). Work on projects

Week 5: Image (2D) Pipeline

16.10 (2h) Pipeline for Artworks photographs. Image banks and phototarchives. Photography as documentation. Scanning techniques for photographs. Segmentation. Visual similarity vs visual connections.

17.10 (4h) Introduction to deep learning analysis of image similarity. Exercises with the Replica database and search engine. 5 mn presentation. Work on projects.

Week 6: Maps (2D) Pipeline

23.10 (2h) What are cartographic documents. Exercice on ancient maps. History of cartography. Odometry. Triangulation. Coordinate systems. Metric systems. Projection. Cadaster. Aerial photography.

24.10 (1-2h) Introduction to GIS. Points, Lines, Polygons. Coordinate Sytems. Georefencing exercice (2-3h) Work on Projects.

Week 7: Object/Environment (3D) Pipeline

30.10 (2h) Pipeline for 3D spaces. Modelling vs Sampling : Part I : Modelling. Photogrammety. Demo Sketchup. Model-based Procedural methods. Architectural grammars. Class I and Class II elements. The question of realism. Part II : Sampling. Principles of Phtogrammetry

31.10 (2h) Photogrammetric tutorial. Video to 3D pipelines. 3D modelisations of past years. (2h) Introduction to the Mirror Worlds concept

Part II : Algorithms

Week 8 : Deep Learning algorithms

06.11 (2h) A panorama of Deep learning methods. Successes. Fundamental principles. Neurons. Receptive Fields. Hierarchical representation / texture. Gradient descent. Credit Assignment Path. Most important architectures. Convolutional neural networks. Recurrent neural networks. Siamese Networks. Word2Vec. Generative Adversarial Networks. Style Transfer. Importance of Deep learning for Digital Humanities. Can Deep Learning networks and Big Data of the Past lead to new forms of Artificial Intelligence ?

07.11 (2h) Computer vision and deep learning tutorial (Sofia). Conditional Random Fields (CRF) tutorial. Both are available on this Github repository.

Week 9 : Project

13.11 (2h) Midterm presentation (10%)

Time First student name Second student name Third student name Project name
10:15-10:30 Leonore Guillain Haeeun Kim Liamarcia Bifano Humans of Paris 1900
10:30-10:45 Arthur Parmentier Bertil Wicht - Book editing in Paris 1847
10:45-11:00 Guilhem Sicard Todor Manev - Love2Late
11:15-11:30 Giacomo Alliata Andrea Scalisi - Influencers of the past
11:30-11:45 Robin Szymczak Cédric Tomasini - Virtual Louvre


14.11 (4h) Presentation of 3D models, Project development

Week 10 : Knowledge modelling

20.11 (2h) The beauty of Knowledge modelling. Tables. Databases. Semantic web, Ontologies, URI, RDF, CIDOC-CRM, How to code event, places and influence. Metaknowledge. The Hypergraph

21.11 (4h) (a) Exercise in semantic modelling and inference (Maud). Graph writing. Presentation of some interesting ontologies: SKOS, VIAF, Geonames, TGN, W3C Time Ontology. SPARQL and SPARQL endpoint. Exercice on SPARQL endpoints: DBPedia [1], Talk of Europe [2], Persée [3], Le Temps ARchive [4]. (b) Work on project, preparation of presentation

-- Project plan and milestones deliverable on the Wikipage of each project (10%)

Part III : Platform management

Week 11 : Data Management

27.11 (2h) Work on project

28.11 (4h) Data Management  : FAIR principle, Creative Commons, Data Management models, Sustainability, Right to Forgotten. Management of uncertainty, incoherence and errors. Iconographic principle of precaution (2h) Work on project (2h)

Week 12 : User Management

04.12 (2h) User Management : Part I: Persona. Part II: Motivation and onboarding dynamics. Three case studies: Twitter. Quora. Wikipedia. Part III: "Wisdom" of the crowds. Collectivism vs Liberalism. Open source as a form of liberalism for engineering. The ambiguous of fork. Part IV: The "power" of the crowds. Mechanical Turk. Crowdflower. Crowdfunding

05.12 (4h) Bot Management : Three case studies on bot management : Twitter, Wikipedia, Google. (2) Work on project (2)

Week 13 : Work on projects

11.12 (2h) Work on project

12.12 (4h) work on project

-- Deadline for GitHub repository (10%)

-- Deadline for Report writing (40%)

Week 14 : Exam

18.12 (2h) Final project presentation (20%)

19.12 (2h) ----

Resources

Assessment and Notation grid

  • 2 oral presentations (30%)
    • 1 midterm presentation of the project (10%)
    • 1 final discussing the project result (20%)
  • Written deliverables (Wiki writing) (40%)
  • Quality of the project (30%)

2 collective oral presentations (30%)

Midterm presenting the project planning (10%)

10' max presentation + 5' questions

Notation grid :

  • The presentation contains a planning (4)
  • + 0.5 The slides are clear and well presented
  • + 0.5 The oral presentation is dynamic and fluid
  • + 0.5 The planning is realistic.
  • + 0.5 The students answer well to the questions

Final discussing the project result (20%)

10-15' for presentation and 5-10' for questions

Notation grid :

  • The presentation presents the results of the project (4)
  • + 0.5 The slides are clear and well presented
  • + 0.5 The oral presentation is dynamic and fluid
  • + 0.5 The results are well discussed
  • + 0.5 The students answer well to the questions

Written deliverables (Wiki writing) (40%)

  • Projet plan and milestones (10%) (>300 words)
  • Historical introduction to the map (5%) (>200 words)
  • Detailed description of the methods (10%) (>500 words)
  • Quality assessment (10%) (>300 words)
  • Motivation and description of the website (5%) (>200 words)

Production (30%)

  • Quality of the realisation 20%
  • Code deliverable on github 10%