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


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


Rooms: Wednesday (CMN1113) and Friday (CM1104)
Rooms: Wednesday (CM1110) and Thursday (BC03)


==Links==
==Links==
 
*[https://www.dropbox.com/scl/fi/j0xoe45dp0km4do41kk9c/FDH_Textbook.pdf?rlkey=mr5hdplu5ai7qts3tk0n4k5a0&dl=0 Textbook]
*[https://www.dropbox.com/scl/fo/tu5waw0623hcp4537lx6u/AKx-eznaH6BRddo1goaF7OE?rlkey=jiewdfpk5ysyv92m1817sk5qc&st=01697apo&dl=0 Slides]
*[https://annuel2.framapad.org/p/fdh Framapad]
*[https://pov-dev.up.railway.app/ Application Parcels of Venice (Dev version)]
*[https://pov.up.railway.app/ Application Parcels of Venice (Stable version)]
*[[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://moodle.epfl.ch/course/view.php?id=15281 Moodle]
*[https://www.getpostman.com API Software]
*[https://annuel2.framapad.org/p/fdh Framapad]
*[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://www.mediawiki.org/wiki/Help:Formatting Wiki Syntax]
*[https://www.getpostman.com API Software]
*[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]
-->


==Summary==
==Summary==
Line 23: Line 36:


==Plan ==
==Plan ==
=== Introduction ===
=== 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 1 : Structural tensions in Digital Humanities ====
==== Week 2 : Patrimonial Capitalism and Commons  ====
20.09 (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.


22.09 (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 the articles by the student followed by peer-review by another student (enriching, completing references). Each student picks a DH person and DH concept, write a Wiki page for each (30 mn + 30 mn). Each student chooses another person and another concept among the ones already covered, enrich with complementary information and references (20 mn + 20 mn)
18.09 :   
* 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]].
* 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.


(25.09 2pm : Experiment with Digital Art History interface INN116)
* 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.


==== Week 2 : Patrimonial capitalism and common goods  ====
* 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.


27.09 (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.
19.09 :


29.09 (4h) Forum ArtTech (Rolex Learning Center). Mininig Big Data of the Past. Patrimonial capitalism and businesses opportunities. Examples of FamilySearch, myHeritage, Corbis.
* FDH 1-6 Anatomy of a large-scale project (1h) Venice Time Machine. European Time Machine.  
* Venice Datasets


=== Part I : Pipelines ===
=== Part II : Pipelines ===


==== Week 3:  Documents pipeline ====
==== Week 3:  Digitisation  ====


04.10 (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.  
25.09 :


06.10 (2h) Pipeline for Written documents.  Part I: Standards. Open Annotation Data Model. Shared Canvas. Part 2: Regulated Representations. DHCanvas. (2h) Presentation of the [[Projects]]. Presentation of the main databases used in the course and [[ClioWire]] platform.
* FDH 1-7 Venice Data presentation (Paul Guhennec)


==== Week 4: Artworks Pipeline ====
26.09 :


11.10 (2h) Pipeline for Artworks photographs. Image banks and phototarchives. Scanning techniques for photographs. Segmentation. Visual similarity vs visual connections.  
* (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.


13.10 (4h)  Introduction to deep learning approaches. Exercises with the Replica database and search engine. 5 mn presentation. Work on projects. Formation of the groups.
* (2h)  Project presentation by prof and TA.


==== Week 5: Maps Pipeline ====
==== Week 4: Writing Systems and Text Encoding  ====


18.10 (2h) What are cartographic documents. Exercice on ancient maps. History of cartography. Odometry. Triangulation. Coordinate systems. Metric systems. Projection. Cadaster. Aerial photography.
2.10 :


20.10 (4h) (a) Introduction to GIS. Points, Lines, Polygons. Coordinate Sytems. QGIS Hands On. Exercise on Venetian cadaster (Bastien).  (b) Oral presentation of project plan
(2h) FDH 2-3 : Writing Systems


==== Week 6: 3D Pipeline ====
3.10 :
- (2h) FDH 2-4 : Text Encoding


25.10 (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.  
* (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].


27.10 (4h) Part II : Sampling.  Photogrammetric tutorial (Nils)
==== Week 5: Text Processing and Understanding ====


=== Part II : Algorithms ===
9.10 :


==== Week 7 : Deep Learning algorithms ====
(2h) FDH 2-5 Text Processing : Diachronic and synchronic analysis. n-grams, TF-IDF, Topic Modeling, Word Space Models and Word embeddings (2h)


01.11 (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 ?
10.10 :


03.11 (2h) Machine vision tutorial (Benoit, Sofia). Introduction to Anaconda, Jupyter, TensorFlow. Deep learning in practice. (2h) Work on bibliography
(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 8 : Knowledge modelling ====
==== Week 6: Images  ====


08.11 (2h) The beauty of Knowledge modelling. Tables. Databases. Semantic web, Ontologies, URI, RDF, CIDOC-CRM, How to code event, places and influence. Metaknowledge
16.10 :


10.11 (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
(2h) FDH 2-7 : Image systems.  


-- Deadline Bibliography and discussion of the state of the art (10%)
17.10 :


==== Week 9 : Project ====
(2h) FDH 2-8 : Image processing (2h) Work on project.


15.11 (2h) '''Midterm presentation with project planning and prior art''' (10%)
(FDH 2-9 : Image understanding not done this year)


17.11 (4h)  Project development


-- Deadline for Introduction/Motivation of individual essay (15%)
==== Week Off ====


=== Part III : Platform management ===
23.10 :


22.11 (2h) Data Management  : Computing infrastructure,  Data Management models, Sustainability. Apps. Management of uncertainty, incoherence and errors. Iconographic principle of precaution. Example of Wikipedia and Europeana.
No course


24.11 (4h) VENICE
24.10 :


29.11 (2h) User Management : Representation, Rights, Traceability, Vandalism, Motivation, Negotiation spaces. Right to be forgotten.
No Course


01.12 (4h) Testing phase
==== Week 7: Maps ====


-- Deadline peer-grading (5%)
30.10 :


06.12 (2h) Bot Management : Versioning. Open source repositories.
(2h) FDH-2-10 Map systems


08.12 (4h) Testing phase and report writing
31.10 :


13.12 (2h) Report writing
(2h) FDH-2-11 Map processing (Remi Petitpierre and Beatrice Vaienti) presentations
(2h) Work on project


-- Deadline for GitHub repository (10%)
==== Week 8: 3D Models  ====


-- Deadline Detailed description of the methods (10%) (>500 words)
06.11 :
(2h) FDH-2-12: 3D Models Systems and Encoding
07.11 :


-- Deadline Quantitive analysis of the performances (10%) (>300 words)
(2h) FDH-2-13: 3D Models Processing : Alex Rusnak Thesis
  (2h)Work on project


-- Deadline for discusison of future work individual essay (15%)
=== MidTerm Presentations ===


15.12 (4h) Final project presentation (20%) and Discussion of the collective paper
13.11 :


==References==
No course


=== Key Figures ===
14.11 Midterm presentations
Identity map (Cardon)


Maps for Big Data Digital Humanities (Kaplan)
{|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
|}


Semiotic Triangle (McCloud)
{|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
|}


Image similarity
=== Part III : Knowledge modelling and processing ===


Uncanny Valley (Mori)
==== Week 9 : Semantic modelling, Rule systems, simulations and parallel worlds  ====


=== Databases ===
20.11 :


(Page to be created indicating characteristics, quantity and copyright)
- (1h) FDH-3-0 Summary of the concept viewed so far and introduction to part 3


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


Cini Photoarchive
21.11 :


Venice Time Machine documents
(2h) FDH 3-2 Universal Ontologies


Scans of Acedemic Book and journals about Venice
(2h) FDH 3-3 Rule systems, simulations and parallel worlds


Linked Book
 
==== 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%)
 
{|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
|}
 
{|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
|}
 
== 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 ==


The final grade is based on 65% collective work and 35% individual work
 
* (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%) ===
=== 2 collective oral presentations (30%) ===


* 1 10' midterm presenting the project planning and prior art (10%)
==== Midterm presenting the project planning (10%) ====
* 1 20' final discussing the project result (20%)
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


=== Collective Written deliverables (Wiki writing) (30%) ===
==== Final discussing the project result (20%) ====
* Bibliography and discussion of the state of the art (10%) (>300 words)
10-15' for presentation and 5-10' for questions
* Detailed description of the methods (10%) (>500 words)
* Quantitive analysis of the performances (10%) (>300 words)


=== Collective Code deliverable (5%) ===
Notation grid :
* Organisation of the GitHub repository (5%)
*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


=== Individual essay (Word or Open Office) ===
=== Written deliverables (Wiki writing) (20%) ===
* Introduction/Motivation on the relevance of ClioWire in the Digital Humanities landscape and Beyond (15 %) (> 500 words)


Notation grid :
* Project plan and milestones (5%) (>300 words)
* A text on the relevance is produced (4)
* Motivation and description of the deliverables (5%) (>300 words)
* + 1 The relevance of Cliowire is well argued
* Detailed description of the methods (5%) (>500 words)
* + 1 The text is clear
* 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%) ===


=== Discussion and Future Work  (15%) (> 500 words) ===
* A series of questions on the course to ensure the core concepts are understood.
=== Peergrading (5%) ===

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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