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Professor: [http://people.epfl.ch/frederic.kaplan Frédéric Kaplan]
Professor: [http://people.epfl.ch/frederic.kaplan Frédéric Kaplan]


Assistant: [https://people.epfl.ch/vincent.buntinx Vincent Buntinx]
Assistants: Alexander Rusnak, Tristan Karch, Tommy Bruzzese


Rooms: Wednesday (CM1113) and Friday (CM1104 and DIA005)
Rooms: Wednesday (CM1110) and Thursday (BC03)


==Links==
==Links==
*[https://conference.timemachine.eu Time Machine Conference 2018]
*[https://www.dropbox.com/scl/fi/j0xoe45dp0km4do41kk9c/FDH_Textbook.pdf?rlkey=mr5hdplu5ai7qts3tk0n4k5a0&dl=0 Textbook]
*[https://moodle.epfl.ch/course/view.php?id=15281 Moodle]
*[https://www.dropbox.com/scl/fo/tu5waw0623hcp4537lx6u/AKx-eznaH6BRddo1goaF7OE?rlkey=jiewdfpk5ysyv92m1817sk5qc&st=01697apo&dl=0 Slides]
*[https://annuel2.framapad.org/p/fdh Framapad]
*[https://annuel2.framapad.org/p/fdh Framapad]
*[https://www.mediawiki.org/wiki/Help:Formatting Wiki Syntax]
*[https://pov-dev.up.railway.app/ Application Parcels of Venice (Dev version)]
*[https://pov.up.railway.app/ Application Parcels of Venice (Stable version)]
*[[Projects]]
*[[Projects]]
* Sources
** [https://www.dropbox.com/scl/fi/oekkk9ts0pnjyiqaezz7m/Toponomastica-Veneziana.pdf?rlkey=wfky7ht9ckxa1w1qo6c69bo98&dl=0 Tassini PDF], [https://www.dropbox.com/scl/fi/lfu8ugk25re333gca65g7/Toponomastica-Veneziana_with_pages_delim.txt?rlkey=fouht7678vs102918hn9fuxs1&dl=0 Tassini OCR]
** [https://www.dropbox.com/scl/fo/netqhm40dyw046withu8q/AJO_CPupJwuLw4Zvg1JF2jc?rlkey=ldswph81gb0n9xgi5qzz1b1i8&dl=0 Guido Commerciale PDF]
** [https://onlinebooks.library.upenn.edu/webbin/metabook?id=sanudodiary Sanudo PDF]
<!--  
<!--  
*[https://conference.timemachine.eu Time Machine Conference 2018]
*[https://cliowire.dhlab.epfl.ch Cliowire]
*[https://cliowire.dhlab.epfl.ch Cliowire]
*[https://www.getpostman.com API Software]
*[https://www.getpostman.com API Software]
*[https://gallica.bnf.fr/accueil/?mode=desktop Gallica]
*[https://docs.opencv.org/3.1.0/db/d27/tutorial_py_table_of_contents_feature2d.html Feature matching tutorial]
*[[Feature matching code]]
*[https://www.mediawiki.org/wiki/Help:Formatting Wiki Syntax]
*[https://github.com/tootsuite/documentation/blob/master/Using-the-API/API.md API documentation]
*[https://github.com/tootsuite/documentation/blob/master/Using-the-API/API.md API documentation]
*[[3D modelisations]]
*[http://fdh.epfl.ch/peergrading/pg.php peer-grading]
*[http://fdh.epfl.ch/peergrading/pg.php peer-grading]
-->
-->
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==Plan ==
==Plan ==
=== Introduction ===
=== Part I : Concepts ===


==== Week 1 : Structural tensions in Digital Humanities ====
==== Week 1 : What are Digital Humanities? ====
19.09 (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.


21.09 (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 on the concepts of Linguistic Capitalism, Big data of the past, Crowdsifting and Thick mapping
11.09 :
<!--
 
* DH historical figures: [[Roberto Busa]], H.G. Wells, [[Paul Otlet]], [[Emmanuel Le Roy Ladurie]], [[Aby Warburg]], Otto Bettmann, [[Tim Berners Lee]], Jimmy Wales, [[Elisée Reclus]], Albert Khan, [[Jules Maciet]].  
(2h) Welcome and Introduction to the course
* DH concepts and related : [[Distant Reading]], Regulated Representations, Pattern, Culturomics, Ubiquitous Scholarship, Gamification, Thick Mapping, Design fiction, New Aesthetics, [[Skeuomorphism]], Digital Aura, [[Digital Heritage]], Attention Economy, [[Folksonomy]], Linguistic Capitalism, Open Access, Redocumentation, Open Hardware, Attention backbone, Opinion Mining, Topic Modelling, [[Gazetteer]], [[Uberisation]], Crowsifting, [[Copyleft]], Onboarding.
* FDH-0 (1h)  Introduction to the course and Digital Humanities, structure of the course. Introduction to Framapad and Slido with a simple exercise. Principle of collective note talking and use in the course. State of the Digital Humanities at EPFL.
-->
 
12.09 :
 
(4h) What are Digital Humanities? What is their object of study?
* FDH-1-1 (1h) What Are Digital Humanities :  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.
* FDH-1-2 (1h) Digital Humanities as a field : Big Data Digital Humanities vs Small Data Digital Humanities. The 3 circles.  Exercise on relationship between elements in Digital Culture schema.
* FDH-1-3 (2h) Big Data of the Past. Data acceleration regime. Inferred Patterns. Redocumentation. Fictional Spaces.
 
==== Week 2 :  Patrimonial Capitalism and Commons  ====
 
18.09 :
 
* FDH 1-4 Patrimonial Capitalism (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.
 
* FDH 1-5 The Commons (1h) What are the commons ? What is the public domains ? History and evolution. Copyright overreaching. Frontal collision. Governing with the commons.
 
19.09 :
 
* FDH 1-6 Anatomy of a large-scale project (1h) Venice Time Machine. European Time Machine.
* Venice Datasets
 
=== Part II : Pipelines ===
 
==== Week 3:  Digitisation  ====
 
25.09 :
 
* FDH 1-7 Venice Data presentation (Paul Guhennec)
 
26.09 :
 
* (2h) FDH 2-1 Introduction to the Digitization Process. Document digitization as a problem of conversion of dimensions. Digitization is logistic optimization. Alienation. Digitization on demand. FDH 2-2 Document Structure. General presentation of the pipeline. Content and Structure. Circulation. Standards. Open Annotation Data Model. Shared Canvas. IIIF.
 
* (2h)  Project presentation by prof and TA.
 
==== Week 4: Writing Systems and Text Encoding  ====
 
2.10 :
 
(2h) FDH 2-3 : Writing Systems
 
3.10 :
- (2h) FDH 2-4 : Text Encoding
 
* (2h)  [[Projects]] presentations. 5' per project with max 3 slides. Write to Tristan before the course. You can find a group using the [https://annuel2.framapad.org/p/fdh framapad].
 
==== Week 5: Text Processing and Understanding ====
 
9.10 :
 
(2h) FDH 2-5 Text Processing : Diachronic and synchronic analysis. n-grams, TF-IDF, Topic Modeling, Word Space Models and Word embeddings (2h)
 
10.10 :
 
(2h) FDH 2-6 Text Understanding : Close, surface, distant and machine reading, Information extraction, Named Entities, Resources, Large-Scale Projects (2h)  Work on Project (2h).
 
==== Week 6:  Images  ====
 
16.10 :
 
(2h) FDH 2-7 : Image systems.
 
17.10 :
 
(2h) FDH 2-8 : Image processing  (2h) Work on project.
 
(FDH 2-9 : Image understanding not done this year)
 
 
==== Week Off  ====
 
23.10 :
 
No course
 
24.10 :
 
No Course
 
==== Week 7: Maps ====


==== Week 2 : Patrimonial capitalism and common goods  ====
30.10 :


26.09 (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 FET Flagship and mutualised infrastructure approach. (1h) General presentation of the Time Machine pipeline at the Datasquare / ArtLab pavillon.
(2h) FDH-2-10 Map systems


28.09 (4h) Paris : Bibliothèque nationale de France.
31.10 :


=== Part I : Pipelines ===
(2h) FDH-2-11 Map processing (Remi Petitpierre and Beatrice Vaienti) presentations
(2h) Work on project


==== Week 3:  Documents pipeline ====
==== Week 8: 3D Models ====


03.10 (2h) The Digitization Process and Pipelines. What is a document? What is a digital image? Exercise on Book Scanners typologies. Document digitisation as a problem of conversion of dimensions. Digitisation is logistic optimization. Alienation. Digitisation on demand. Fedorov's notion of optimal experiment.  
06.11 :
(2h) FDH-2-12: 3D Models Systems and Encoding
07.11 :


05.10 (2h) Pipeline for Written documents.  Part I: Standards. Open Annotation Data Model. Shared Canvas. Part 2: Regulated Representations. DHCanvas. (2h) Presentation of the [[Projects]]. Research of 5 maps of maps series. Formation of the groups
(2h) FDH-2-13: 3D Models Processing : Alex Rusnak Thesis
  (2h)Work on project


==== Week 4: Artworks Pipeline ====
=== MidTerm Presentations ===


10.10 (2h) Pipeline for Artworks photographs (Benoit Seguin). Image banks and phototarchives. Scanning techniques for photographs. Segmentation. Visual similarity vs visual connections.  
13.11 :


12.10 (4h)  Introduction to deep learning approaches (Benoit Seguin). Exercises with the Replica database and search engine. 5 mn presentation. Work on projects. Formation of the groups.
No course


==== Week 5: Maps Pipeline ====
14.11 Midterm presentations


17.10 (2h) What are cartographic documents. Exercice on ancient maps. History of cartography. Odometry. Triangulation. Coordinate systems. Metric systems. Projection. Cadaster. Aerial photography.
{|class="wikitable"
! style="text-align:center;"| Time
! Project name
|-
|10:20-10:40
| Group 8
|-
|10:40-11:00
| Group 9
|-
|11:00-11:20
| Group 7
|-
|11:20-11:40
| Group 6
|-
|11:40-12:00
| Group 3
|}


19.10 (4h) (a) Introduction to GIS. Points, Lines, Polygons. Coordinate Sytems. QGIS Hands On. Exercise on Venetian cadaster (Bastien).  (b) Oral presentation of project plan
{|class="wikitable"
! style="text-align:center;"| Time
! Project name
|-
|13:15-13:35
|  Group 2
|-
|13:35-13:55
|  Group 5
|-
|13:55-14:15
|  Group 4
|-
|14:15-14:35
|  Group 1
|-
|14:35-14:55
|  Group 10
|}


==== Week 6: 3D Pipeline ====
=== Part III : Knowledge modelling and processing ===


24.10 (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.
==== Week 9 : Semantic modelling, Rule systems, simulations and parallel worlds  ====


26.10 (4h) Part II : Sampling.  Photogrammetric tutorial (Nils)
20.11 :


=== Part II : Algorithms ===
- (1h) FDH-3-0 Summary of the concept viewed so far and introduction to part 3


==== Week 7 : Deep Learning algorithms ====
- (1h) FDH-3-1 Semantic modelling. RDF, Metaknowledge


31.10 (2h) Time Machine 2018 http://conference.timemachine.eu
21.11 :


02.11 (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 ? Machine vision tutorial (Benoit, Sofia). Introduction to Anaconda, Jupyter, TensorFlow. Deep learning in practice.
(2h) FDH 3-2 Universal Ontologies


==== Week 8 : Knowledge modelling ====
(2h) FDH 3-3 Rule systems, simulations and parallel worlds


07.11 (2h) The beauty of Knowledge modelling. Tables. Databases. Semantic web, Ontologies, URI, RDF, CIDOC-CRM, How to code event, places and influence. Metaknowledge


09.11 (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 [http://dbpedia.org/sparql], Talk of Europe  [http://linkedpolitics.ops.few.vu.nl/yasgui/index.html], Persée [http://data.persee.fr/explorer/ ], Le Temps ARchive [http://iccluster052.iccluster.epfl.ch:8899/sparql]. (b) Work on project, preparation of presentation
==== Week 10 : Non conceptual knowledge systems and topological data science ====


-- First deliverable for project (10%)
27.11 :


==== Week 9 : Project  ====
(2h) FDH 3-4 Non conceptual knowledge systems


14.11 (2h) '''Midterm presentation with project planning and prior art''' (10%)
28.11 :


16.11 (4h) Project development
(2h) Work on Projects / Publication of Study Guide
(2h) Work on Projects


-- Deadline for Introduction/Motivation of  individual essay (15%)
=== Part IV : Platforms ===


=== Part III : Platform management ===
==== Week 12 : Data, User and Bot  Management  ====


4.12 :


==== Week 10 : Data Management  ====
(2h) Study Guide Discussion


5.12 :


21.11 (2h) 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


23.11 (4h) Exercises in Venice. Visit of the State Archives and Cini Foundation. Photogrammetric survey
(2h) Work on Project


==== Week 11 : User Management ====
==== Course Exam and Project work ====


28.11 (2h) Programming and Testing phase : Production of first Pulses
11.12 In class exam


30.11 (4h) 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
12.12 Work on project


==== Week 12 : Bot Management  ====
==== Final Week : Project Presentation ====


05.12 (2h) Bot Management : Three case studies on bot management : Twitter, Wikipedia, Google.
18.12


07.12 (4h) Testing phase and report writing
-- Due: GitHub repository (10%)


12.12 (2h) Report writing
-- Due: Report writing (40%)


14.12 (4h) Discussion of the collective paper
19.12


-- Deadline for GitHub repository (10%)
(4h) Final project presentation  (20%)


19.12 (2h) Final project presentation (20%)
{|class="wikitable"
! style="text-align:center;"| Time
! Project Name
|-
| 10:15 - 10:40
| Group 1
|-
| 10:40 - 11:05
| Group 2
|-
| 11:05 - 11:30
| Group 3
|-
| 11:30 - 11:55
| Group 4
|-
| 11:55 - 12:20
| Group 5
|}


21.12 (2h) Final project presentation (20%)
{|class="wikitable"
! style="text-align:center;"| Time
! Project Name
|-
| 1:15 - 1:40
| Group 6
|-
| 1:40 - 2:05
| Group 7
|-
| 2:05 - 2:30
| Group 8
|-
| 2:30 - 2:55
| Group 9
|-
| 2:55 - 3:20
| Group 10
|}


==References==
== Resources ==
*[https://gallica.bnf.fr/accueil/?mode=desktop Gallica]
*[https://docs.opencv.org/3.1.0/db/d27/tutorial_py_table_of_contents_feature2d.html Feature matching tutorial]
*[[Feature matching code]]
*[https://www.mediawiki.org/wiki/Help:Formatting Wiki Syntax]
*[https://annuel2.framapad.org/p/r.664b6addb1d73a5242943f306814e898 Introduction to Rhino and Grasshopper]


==Assessment and Notation grid ==
==Assessment and Notation grid ==




* 2 oral presentations (30%)
* (Group work) 2 oral presentations (30%)
** 1 midterm presentation of the project plan (10%)
** 1 midterm presentation of the project (10%)
** 1 final discussing the project result (20%)
** 1 final discussing the project result (20%)
* Written deliverables (Wiki writing) (40%)
* (Group work) Written deliverables (Wiki writing) (20%)
* Quality of the project (30%)
* (Group work) Quality of the project (30%)
* (Individual work) Exam on Course Content (20%)


=== 2 collective oral presentations (30%) ===
=== 2 collective oral presentations (30%) ===


==== Midterm presenting the project planning and prior art 10' (10%) ====
==== Midterm presenting the project planning (10%) ====
10' max presentation + 5' questions


Notation grid :
Notation grid :
*The presentation contains a planning and discussion of prior art (4)
*The presentation contains a planning (4)
* + 0.5 The slides are clear and well presented
* + 0.5 The slides are clear and well presented
* + 0.5 The oral presentation is dynamic and fluid
* + 0.5 The oral presentation is dynamic and fluid
Line 148: Line 322:
* + 0.5 The students answer well to the questions
* + 0.5 The students answer well to the questions


==== Final discussing the project result 20' (20%) ====
==== Final discussing the project result (20%) ====
10-15' for presentation and 5-10' for questions


Notation grid :
Notation grid :
Line 154: Line 329:
* + 0.5 The slides are clear and well presented
* + 0.5 The slides are clear and well presented
* + 0.5 The oral presentation is dynamic and fluid
* + 0.5 The oral presentation is dynamic and fluid
* + 0.5 The results are will discussed
* + 0.5 The results are well discussed
* + 0.5 The students answer well to the questions
* + 0.5 The students answer well to the questions


=== Written deliverables (Wiki writing) (40%) ===
=== Written deliverables (Wiki writing) (20%) ===
 
* Project plan and milestones (5%) (>300 words)
* Motivation and description of the deliverables (5%) (>300 words)
* Detailed description of the methods (5%) (>500 words)
* Quality assessment and discussion of limitations  (5%) (>300 words)


* Projet plan and milestones (10%) (>300 words)
The indicated number of words is a minimal bound. Detailed description can in particular be extended if needed.
* Historical introduction to the map (10%) (>200 words)
* Detailed description of the extraction methods (10%) (>500 words)
* Quantitive analysis of the performances of extraction  (10%) (>300 words)
* Motivation and description of the services (10%) (>200 words)


=== Production  (30%) ===
=== Production  (30%) ===
Line 169: Line 345:
* Quality of the realisation 20%
* Quality of the realisation 20%
* Code deliverable on github  10%
* Code deliverable on github  10%
=== Exam on Course Content  (20%) ===
* A series of questions on the course to ensure the core concepts are understood.

Latest revision as of 10:48, 17 December 2024

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

Contact

Professor: Frédéric Kaplan

Assistants: Alexander Rusnak, Tristan Karch, Tommy Bruzzese

Rooms: Wednesday (CM1110) 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

Part I : Concepts

Week 1 : What are Digital Humanities?

11.09 :

(2h) Welcome and Introduction to the course

  • FDH-0 (1h) Introduction to the course and Digital Humanities, structure of the course. Introduction to Framapad and Slido with a simple exercise. Principle of collective note talking and use in the course. State of the Digital Humanities at EPFL.

12.09 :

(4h) What are Digital Humanities? What is their object of study?

  • FDH-1-1 (1h) What Are Digital Humanities : 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.
  • FDH-1-2 (1h) Digital Humanities as a field : Big Data Digital Humanities vs Small Data Digital Humanities. The 3 circles. Exercise on relationship between elements in Digital Culture schema.
  • FDH-1-3 (2h) Big Data of the Past. Data acceleration regime. Inferred Patterns. Redocumentation. Fictional Spaces.

Week 2 : Patrimonial Capitalism and Commons

18.09 :

  • FDH 1-4 Patrimonial Capitalism (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.
  • FDH 1-5 The Commons (1h) What are the commons ? What is the public domains ? History and evolution. Copyright overreaching. Frontal collision. Governing with the commons.

19.09 :

  • FDH 1-6 Anatomy of a large-scale project (1h) Venice Time Machine. European Time Machine.
  • Venice Datasets

Part II : Pipelines

Week 3: Digitisation

25.09 :

  • FDH 1-7 Venice Data presentation (Paul Guhennec)

26.09 :

  • (2h) FDH 2-1 Introduction to the Digitization Process. Document digitization as a problem of conversion of dimensions. Digitization is logistic optimization. Alienation. Digitization on demand. FDH 2-2 Document Structure. General presentation of the pipeline. Content and Structure. Circulation. Standards. Open Annotation Data Model. Shared Canvas. IIIF.
  • (2h) Project presentation by prof and TA.

Week 4: Writing Systems and Text Encoding

2.10 :

(2h) FDH 2-3 : Writing Systems

3.10 :

- (2h) FDH 2-4 : Text Encoding

  • (2h) Projects presentations. 5' per project with max 3 slides. Write to Tristan before the course. You can find a group using the framapad.

Week 5: Text Processing and Understanding

9.10 :

(2h) FDH 2-5 Text Processing : Diachronic and synchronic analysis. n-grams, TF-IDF, Topic Modeling, Word Space Models and Word embeddings (2h)

10.10 :

(2h) FDH 2-6 Text Understanding : Close, surface, distant and machine reading, Information extraction, Named Entities, Resources, Large-Scale Projects (2h) Work on Project (2h).

Week 6: Images

16.10 :

(2h) FDH 2-7 : Image systems.

17.10 :

(2h) FDH 2-8 : Image processing (2h) Work on project.

(FDH 2-9 : Image understanding not done this year)


Week Off

23.10 :

No course

24.10 :

No Course

Week 7: Maps

30.10 :

(2h) FDH-2-10 Map systems

31.10 :

(2h) FDH-2-11 Map processing (Remi Petitpierre and Beatrice Vaienti) presentations (2h) Work on project

Week 8: 3D Models

06.11 :

(2h) FDH-2-12: 3D Models Systems and Encoding 07.11 :

(2h) FDH-2-13: 3D Models Processing : Alex Rusnak Thesis 

  (2h)Work on project

MidTerm Presentations

13.11 :

No course

14.11 Midterm presentations

Time Project name
10:20-10:40 Group 8
10:40-11:00 Group 9
11:00-11:20 Group 7
11:20-11:40 Group 6
11:40-12:00 Group 3
Time Project name
13:15-13:35 Group 2
13:35-13:55 Group 5
13:55-14:15 Group 4
14:15-14:35 Group 1
14:35-14:55 Group 10

Part III : Knowledge modelling and processing

Week 9 : Semantic modelling, Rule systems, simulations and parallel worlds

20.11 :

- (1h) FDH-3-0 Summary of the concept viewed so far and introduction to part 3

- (1h) FDH-3-1 Semantic modelling. RDF, Metaknowledge

21.11 :

(2h) FDH 3-2 Universal Ontologies

(2h) FDH 3-3 Rule systems, simulations and parallel worlds


Week 10 : Non conceptual knowledge systems and topological data science

27.11 :

(2h) FDH 3-4 Non conceptual knowledge systems

28.11 :

(2h) Work on Projects / Publication of Study Guide (2h) Work on Projects

Part IV : Platforms

Week 12 : Data, User and Bot Management

4.12 :

(2h) Study Guide Discussion

5.12 :

(2h) Work on Project

(2h) Work on Project

Course Exam and Project work

11.12 In class exam

12.12 Work on project

Final Week : Project Presentation

18.12

-- Due: GitHub repository (10%)

-- Due: Report writing (40%)

19.12

(4h) Final project presentation (20%)

Time Project Name
10:15 - 10:40 Group 1
10:40 - 11:05 Group 2
11:05 - 11:30 Group 3
11:30 - 11:55 Group 4
11:55 - 12:20 Group 5
Time Project Name
1:15 - 1:40 Group 6
1:40 - 2:05 Group 7
2:05 - 2:30 Group 8
2:30 - 2:55 Group 9
2:55 - 3:20 Group 10

Resources

Assessment and Notation grid

  • (Group work) 2 oral presentations (30%)
    • 1 midterm presentation of the project (10%)
    • 1 final discussing the project result (20%)
  • (Group work) Written deliverables (Wiki writing) (20%)
  • (Group work) Quality of the project (30%)
  • (Individual work) Exam on Course Content (20%)

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) (20%)

  • Project plan and milestones (5%) (>300 words)
  • Motivation and description of the deliverables (5%) (>300 words)
  • Detailed description of the methods (5%) (>500 words)
  • Quality assessment and discussion of limitations (5%) (>300 words)

The indicated number of words is a minimal bound. Detailed description can in particular be extended if needed.

Production (30%)

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


Exam on Course Content (20%)

  • A series of questions on the course to ensure the core concepts are understood.
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