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==Contact==
==Contact==
Professor: [http://people.epfl.ch/frederic.kaplan Frédéric Kaplan]<br>
Professor: [http://people.epfl.ch/frederic.kaplan Frédéric Kaplan]
assistants: ...
 
Assistants: Alexander Rusnak
 
Rooms: Wednesday (CM1110) and Thursday (BC03/BC04)
 
==Links==
*[https://www.dropbox.com/scl/fi/lmievevb2b78lyxoqu130/FDH_Textbook.pdf?rlkey=c6dv6m97sf81l6dcz7nw777j0&dl=0 Textbook]
*[https://www.dropbox.com/scl/fo/w4dcnwdtfd4xq4p0bj3ub/AETpOIrGvsYisZPTbitU4xY?rlkey=heoqml8hsg4judym2yhzrzliy&dl=0 Slides]
*[https://timeatlas.eu Time Atlas (Public version)]
*[https://timeatlas-test.epfl.ch/app/ Time Atlas (Dev version)]
*[[Projects]]
* Sources (2024)
** [https://www.dropbox.com/scl/fi/s4zlaxrpka4w1h7mx6zvu/FDH_Morphographs.zip?rlkey=qp1omyyjj9dqfuko49rrd37tt&dl=0 Morphograph]
* Sources (2024)
** [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://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]
*[http://fdh.epfl.ch/peergrading/pg.php peer-grading]
-->


==Summary==
==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. 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. Students will practise the skills they learn directly analysing and interpreting Cultural Datasets from ongoing large-scale research projects (Venice Time Machine, Swiss newspaper archives).
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 ==
==Plan ==
=== Part I : Concepts and Fields ===
==== Week 1 : What are Digital Humanities? ====
10.09 :
(2h) Welcome and Introduction to the course
* Getting know each others. Presentation of each student with a Photo illustrating their interest or history.
*  Introduction to the structure of the course and the FDH textbook.
11.09 :
What are Digital Humanities? What is their object of study?
* Digital vs Analogue, Abstract vs Concrete, Information, Token, Data/Code equivalence
* Maud Ehrmann, Impresso project and data acceleration regime (2h)
==== Week 2 :  What are Digital Humanities (ctd.) ====
17.09 : 
* Humanities, Hermeneutics. Patterns
18.09 :
Morning :
*  Anatomy of a large-scale project : Venice Time Machine. European Time Machine.
Afternoon :
* Manuel Ehrenfeld / Designing the Time Machine Atlas
* Formation of the groups (ideally 2, max 3 students)
==== Week 3:  Subfields Student Presentation  ====
Groups of students will give a 10-minute presentation, using slides, followed by a 10-minute class discussion on the potential of Big Data in various fields of the Humanities. Presentations may include an overview of existing projects and/or projections of future research in these disciplines. (See graduation system below)
For each subfields, groups must
* Define the specific object of study of the subfield,
* Define the opportunities of a change of scale,
* Illustrate with existing or prospective examples.
24.09 :
* Pierre Vann, Julien Jordan / Architecture/Urbanism
* Anaël Donini, Anastasia Meijer / Applied Sociology
* Eliott Bell, Christophe Bitar / Big Data in History
* Camille Dupre Tabti, Olivia Robles /  Big Data and Musicology
* Néhémie Frei, Niccholas Reiz Art History: Film Studies
25.09 :
Morning :
* Camille Lannoye, Sophia Kovalenko / Art History
* Yibo Yin, Jiajun Shen, Yifan Zhou / Big Data and History
* Marguerite Novikov / Lingustics
* Xiru Wang, Jingru Wnag / Big data and theology
* Jérémy Hugentobler / Archeology
Afternoon
2.15 pm : Projects presentation by prof and TA. Examples of DH projects that can be selected during the course. It is also possible to invent new one. Each student has to select two and present their ideas the following week.
=== Part II : Media Pipelines  ===
==== Week 4: Documents and Writing Systems  ====
01.10 :
(2h) Documents : Definition and Encoding. IIIF Format.
02.10 :
- (2h) Writing Systems
* (2h)  [[Projects]] presentations. Each group present 2 projects. 5' per project with max 3 slides. At the end of the session, the goal is to select one really fitting the taste and skills of the students and the learning ambition of the course.
Vote on skills tutorial for Skills sessions
==== Week 5: Texts ====
08.10 :
(2h) Reading and Text encoding (2h)
09.10 :
(2h) Text Spaces and Text systems
(2h) SKILL SESSION 1 (Vote for the tutorials) (Alex Rusnak)
==== Week 6:  Paintings, Engravings and Photographs  ====
15.10 :
(2h) Image reading. Image space and image systems
16.10 :
(2h) Example of the Replica Pipeline
(2h) SKILLS SESSION 2 (Vote for the tutorials) (Alex Rusnak)
==== Week Off  ====
22.10 :
No course
23.10 :
No Course
==== Week 7: Maps ====
29.10 :
(2h) Are Maps different than images ? Alex Rusnak's Presentation on 2D/3D map encoding
30.10 :
Morning
(2h) Remi Petitpierre presentation : Cartography at scale
Afternoon
2h) Beatrice Vaienti presentation : Genealogies of Jerusalem's maps
==== Week 8: Design and Architecture  ====
05.11 :
(2h) 3D Models Systems and Encoding
06.11 :
(2h) 3D Models Processing
(2h)Work on project : Definition of the MVP (Minimum Viable Product)
=== MidTerm Presentations ===
12.11 :
* You can select the presentation of this course : Music, Poetry, Fashion, Rituals, Web, Diagrams and Tables, Zeitgeist modelling (simulation of the everyday)
* Summary of the concept viewed so far. Publication of the study guide
13.11 Midterm presentations
{|class="wikitable"
! style="text-align:center;"| Time
! Project name
|-
|10:20-10:40
| Group 7
|-
|10:40-11:00
| Group 6
|-
|11:00-11:20
| Group 5
|-
|11:20-11:40
| Group 4
|}
{|class="wikitable"
! style="text-align:center;"| Time
! Project name
|-
|13:15-13:35
|  Group 3
|-
|13:35-13:55
|  Group 2
|-
|13:55-14:15
|  Group 1
|-
|14:15-14:35
|  Group 8
|}
=== Part III : Knowledge modelling and processing ===
==== Week 9 : Semantic modelling, Rule systems, simulations and parallel worlds  ====
19.11 :
- (1h) Semantic modelling. RDF, Metaknowledge
- (1h) Universal Ontologies
20.11 :
(2h)  Rule systems, simulations and parallel worlds
(2h) Preparation of the exam


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. Differences between 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. Big Data Digital Humanities and related maps.


22.09 (4h) Introduction to MediaWiki. Creation of the articles by the student followed by peer-review and collective work. DH historical figures: Roberto Busa, H.G. Wells, Paul Otlet, Emmanuel Le Roy Ladurie, Any Warburg, 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, Right to be forgotten, 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. DH tools: Framapad, MediaWiki, Voyant, OpenRefine, QGIS, Jupyter
==== Week 10 : In class Exam and Non conceptual knowledge systems and topological data science ====


27.09 (2h) Introduction to the DH circle of processing and interpretation (acquisition, processing, analysis, visualisation, UX, interpretation). From data acquisition to new understandings. General presentation of the Time Machine pipeline. Digitisation campaign planning.  
26.11 :


29.09 (4h) Introduction to Python and bot writing (Vincent).
(2h) In class exam


=== Part I : Pipelines ===
27.11 :


04.10 (2h) Pipeline for Written documents (Printed and Handwritten). Scanning techniques for books and documents. Principles of Transcription. Transcription tools. Canvas concept. One canvas multiple images, one image multiple canvas. Short introduction to IIIF. Named Entities, Semantic modelling,Topic and Document modelling.
(2h) Non conceptual knowledge systems
(2h) Work on Projects


06.10 (4h) Presentation of DHCanvas (Orlin). Open Annotation Data Model. Shared Canvas. IIIF. Exercise : Transcription of Venetian document (Maud, Giovanni)
=== Part IV : Platforms ===


11.10 (2h) Pipeline for Maps. Vectorization. Alignment. Homologs Points.
==== Week 12 : Data, User and Agent  Management  ====


13.10 (4h) QGIS Hands On. Exercise on Venetian cadaster (Bastien, Isabella)
03.12 :


18.10 (2h) Pipeline for Artworks photographs. Image banks and phototarchives. Scanning techniques for photographs. Segmentation. Features detection. Detail search.
(2h) Data


20.10 (4h) Exercises with the Replica database and search engine (Benoit, Sofia)
04.12 :


25.10 (2h) Pipeline for 3D spaces. Photogrammety. Diachronic realignment. Multiscale indexation.
(2h) User


27.10 (4h) Photogrammetric tutorial (Nils)
(2h) Agents


=== Part II : Algorithms ===
==== Project work  ====


01.11 (2h) Algorithms for Document processing : Document analysis and Deep learning methods
10.12 Work on project


03.11 (4h) Machine vision tutorial (Benoit, Sofia). Introduction to Jupyter. Deep learning in practice.  
11.12 Work on project


08.11 (2h) Algorithms for Knowledge modelling : Semantic web, ontologies, graph database, homologous points, disambiguation.
==== Final Week : Project Presentation ====


10.11 (4h) Exercise in semantic modelling and inference (Maud)
17.12


15.11 (2h) Algorithms for Generative models and simulation : Rule-based inference, Deep learning based generation
-- Due: GitHub repository
-- Due: Report writing


17.11 (4h) Exercise in deep-learning based generation (Benoit, Sofia)
18.12


=== Part III : Platform management ===
(4h) Final project presentation  (20%)


22.11 (2h) Data Management  : Computing infrastructure,  Data Management models, Sustainability. Apps. Example of Wikipedia and Europeana.
{|class="wikitable"
! style="text-align:center;"| Time
! Project Name
|-
| 10:20 - 10:40
| Group 5
|-
| 10:40 - 11:00
| Group 1
|-
| 11:00 - 11:20
| Group 2
|-
| 11:20 - 11:40
| Group 3
|}


24.11 (4h) Project development in teams
{|class="wikitable"
! style="text-align:center;"| Time
! Project Name
|-
| 13:15 - 13:35
| Group 4
|-
| 13:35 - 13:55
| Group 6
|-
| 13:55 - 14:15
| Group 7
|-
| 14:15 - 14:35
| Group 8
|}


29.11 (2h) User Management : Representation, Rights, Traceability, Vandalism, Motivation, Negotiation spaces
== Various 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]


01.12 (4h) Project development in teams
==Assessment and Notation grid ==


06.12 (2h) Bot Management : Versioning. Open source repositories.


08.12 (4h) Project development in teams
* (Group work) 3 oral presentations (30%)
** Presention of the potential of Big data for a subfield of Digital Humanities (10%)
** 1 midterm presentation of the project (10%)
** 1 final discussing the project result (10%)
* (Group work) Written deliverables (Wiki writing) (20%)
* (Group work) Quality of the project and code (20%)
* (Individual work) Exam on Course Content (30%)


13.12 Exam
=== 3 collective oral presentations (30%) ===


15.12 (4h)


==References==
==== Subfield presentation  (10%) ====
10' max presentation + 5' questions


=== Key Figures ===
Notation grid :
Identity map (Cardon)
*The presentation contains a description of the subfield in relation with Big Data (4)
* + 0.5 The slides are clear and well presented
* + 0.5 The oral presentation is dynamic and fluid
* + 0.5 The applications are relevant
* + 0.5 The students participate well to the collective discussion


Maps for Big Data Digital Humanities (Kaplan)
==== Midterm presenting the project planning  (10%) ====
10' max presentation + 5' questions


Semiotic Triangle (McCloud)
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


Uncanny Valley
==== Final discussing the project result (10%) ====
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


=== Databases ===
=== Written deliverables (Wiki writing) (20%) ===


Le Temps Archives
* 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)


Cini Phototech
The indicated number of words is a minimal bound.


Venice Time Machine documents
=== Quality of the project and code (20%) ===


Scans of Acedemic Book and journals about Venice
* Quality of the realisation 10%
* Code delivered on github  10%


Linked Book
=== Exam on Course Content  (30%) ===


==Notation system==
* A series of questions on the course to ensure the core concepts are understood. [https://www.dropbox.com/scl/fi/9zrr1phua0jm1636ij5mn/FDH_2024_Exam.pdf?rlkey=ad4uj531ddolyklrk9qcntt8k&dl=0 Example from last year]

Latest revision as of 08:50, 18 December 2025

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

Contact

Professor: Frédéric Kaplan

Assistants: Alexander Rusnak

Rooms: Wednesday (CM1110) and Thursday (BC03/BC04)

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 and Fields

Week 1 : What are Digital Humanities?

10.09 :

(2h) Welcome and Introduction to the course

  • Getting know each others. Presentation of each student with a Photo illustrating their interest or history.
  • Introduction to the structure of the course and the FDH textbook.

11.09 :

What are Digital Humanities? What is their object of study?

  • Digital vs Analogue, Abstract vs Concrete, Information, Token, Data/Code equivalence
  • Maud Ehrmann, Impresso project and data acceleration regime (2h)

Week 2 : What are Digital Humanities (ctd.)

17.09 :

  • Humanities, Hermeneutics. Patterns

18.09 :

Morning :

  • Anatomy of a large-scale project : Venice Time Machine. European Time Machine.

Afternoon :

  • Manuel Ehrenfeld / Designing the Time Machine Atlas
  • Formation of the groups (ideally 2, max 3 students)

Week 3: Subfields Student Presentation

Groups of students will give a 10-minute presentation, using slides, followed by a 10-minute class discussion on the potential of Big Data in various fields of the Humanities. Presentations may include an overview of existing projects and/or projections of future research in these disciplines. (See graduation system below)

For each subfields, groups must

  • Define the specific object of study of the subfield,
  • Define the opportunities of a change of scale,
  • Illustrate with existing or prospective examples.

24.09 :

  • Pierre Vann, Julien Jordan / Architecture/Urbanism
  • Anaël Donini, Anastasia Meijer / Applied Sociology
  • Eliott Bell, Christophe Bitar / Big Data in History
  • Camille Dupre Tabti, Olivia Robles / Big Data and Musicology
  • Néhémie Frei, Niccholas Reiz Art History: Film Studies

25.09 :

Morning :

  • Camille Lannoye, Sophia Kovalenko / Art History
  • Yibo Yin, Jiajun Shen, Yifan Zhou / Big Data and History
  • Marguerite Novikov / Lingustics
  • Xiru Wang, Jingru Wnag / Big data and theology
  • Jérémy Hugentobler / Archeology

Afternoon

2.15 pm : Projects presentation by prof and TA. Examples of DH projects that can be selected during the course. It is also possible to invent new one. Each student has to select two and present their ideas the following week.

Part II : Media Pipelines

Week 4: Documents and Writing Systems

01.10 :

(2h) Documents : Definition and Encoding. IIIF Format.

02.10 :

- (2h) Writing Systems

  • (2h) Projects presentations. Each group present 2 projects. 5' per project with max 3 slides. At the end of the session, the goal is to select one really fitting the taste and skills of the students and the learning ambition of the course.

Vote on skills tutorial for Skills sessions

Week 5: Texts

08.10 :

(2h) Reading and Text encoding (2h)

09.10 :

(2h) Text Spaces and Text systems

(2h) SKILL SESSION 1 (Vote for the tutorials) (Alex Rusnak)

Week 6: Paintings, Engravings and Photographs

15.10 :

(2h) Image reading. Image space and image systems

16.10 :

(2h) Example of the Replica Pipeline


(2h) SKILLS SESSION 2 (Vote for the tutorials) (Alex Rusnak)

Week Off

22.10 :

No course

23.10 :

No Course

Week 7: Maps

29.10 :

(2h) Are Maps different than images ? Alex Rusnak's Presentation on 2D/3D map encoding

30.10 :

Morning

(2h) Remi Petitpierre presentation : Cartography at scale

Afternoon

2h) Beatrice Vaienti presentation : Genealogies of Jerusalem's maps

Week 8: Design and Architecture

05.11 :

(2h) 3D Models Systems and Encoding

06.11 :

(2h) 3D Models Processing

(2h)Work on project : Definition of the MVP (Minimum Viable Product)

MidTerm Presentations

12.11 :

  • You can select the presentation of this course : Music, Poetry, Fashion, Rituals, Web, Diagrams and Tables, Zeitgeist modelling (simulation of the everyday)
  • Summary of the concept viewed so far. Publication of the study guide

13.11 Midterm presentations

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

Part III : Knowledge modelling and processing

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

19.11 :

- (1h) Semantic modelling. RDF, Metaknowledge - (1h) Universal Ontologies

20.11 :

(2h) Rule systems, simulations and parallel worlds

(2h) Preparation of the exam


Week 10 : In class Exam and Non conceptual knowledge systems and topological data science

26.11 :

(2h) In class exam

27.11 :

(2h) Non conceptual knowledge systems (2h) Work on Projects

Part IV : Platforms

Week 12 : Data, User and Agent Management

03.12 :

(2h) Data

04.12 :

(2h) User

(2h) Agents

Project work

10.12 Work on project

11.12 Work on project

Final Week : Project Presentation

17.12

-- Due: GitHub repository -- Due: Report writing

18.12

(4h) Final project presentation (20%)

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

Various Resources

Assessment and Notation grid

  • (Group work) 3 oral presentations (30%)
    • Presention of the potential of Big data for a subfield of Digital Humanities (10%)
    • 1 midterm presentation of the project (10%)
    • 1 final discussing the project result (10%)
  • (Group work) Written deliverables (Wiki writing) (20%)
  • (Group work) Quality of the project and code (20%)
  • (Individual work) Exam on Course Content (30%)

3 collective oral presentations (30%)

Subfield presentation (10%)

10' max presentation + 5' questions

Notation grid :

  • The presentation contains a description of the subfield in relation with Big Data (4)
  • + 0.5 The slides are clear and well presented
  • + 0.5 The oral presentation is dynamic and fluid
  • + 0.5 The applications are relevant
  • + 0.5 The students participate well to the collective discussion

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 (10%)

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.

Quality of the project and code (20%)

  • Quality of the realisation 10%
  • Code delivered on github 10%

Exam on Course Content (30%)

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