Study Plan

Here is the overall study plan for 2018-19 followed by the core courses descriptions.

Overall Master Structure:

1st year: 2 semesters of courses (core courses + options).

2nd year: 1 semester of internship and 1 semester of Master Project.

Study Plan in Excel format

Click here to download the study plan in xls format.

UNIL courses

DH Master students can take a selection of UNIL courses outside of the study plan as part of the options group. According to the rules, a maximum of 6 credits can be taken outside of the study plan.

Courses at UNIL are all given in French.

Courses that DH Master students can take for the 2018-2019 academic year at UNIL are the following:

Introduction à l’épistémologie du numérique – Nicolas Baya Laffite, Boris Beaude – Fall – 3 credits
Introduction à l’étude de la culture numérique – Claus Gunti – Fall – 3 credits
Ingénierie documentaire – Michael Piotrowski – Fall – 6 ECTS
Humanités numériques – origines, définitions, développements – Michael Piotrowski – Spring – 3 ECTS

Registering for UNIL courses

Please click here to access the EPFL SAC page that tells you how to register to these courses. Please see “Subjects outside of the study plan”.

Descriptions of Core Courses

Computer Science Block

Applied Data Analysis – Robert West
Thanks to the new software tools that allow easily handling and mining data at scale, today’s data scientists are able to extract invaluable insights from the vast amount of data generated daily. This class teaches the basic techniques and practical skills required to make sense out of such data, and the most acclaimed software tools in the Data Science world.

Foundations of Digital Humanities – Frédéric Kaplan
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, etc.).

Computational Social Media – Daniel Gatica-Perez
The course will present a human-centered view of computational social media. It uses a multidisciplinary approach and integrates concepts from media studies, multimedia information systems, machine learning, and network science to present the socio-technical fundamentals needed to understand the motivations, characterize the behavior, and analyze the content and relations of social media users and communities in sites like Twitter, Facebook, Instagram, and YouTube. Students will become familiar with computational approaches for classification, discovery, and prediction of individual and networked phenomena in social media.

Signal Processing and Machine Learning for Digital Humanities – Andrea Ridolfi and Mathieu Salzmann
The goal of this course is to provide the signal processing and machine learning concepts useful for the future engineers in Digital Humanities. In any data driven research, how to acquire, process, encode, and analyze data is of paramount importance, and this course will cover the necessary tools to solve real-world problems. The digital signal processing topics are sampling and interpolation, quantization, filtering, and encoding. From machine learning, regression and pattern classification methods alongside clustering and other data reduction algorithms will be covered.

Humanities Block

Quantification of User Experience – Dominique Boullier
The public is a phantom (Lippmann) in media as well as in digital humanities, i.e. tracing his/her experience is a constant bet. However, social sciences and cognitive sciences have a long history in capturing and quantifying the user experience. Students will learn to consider the historical and cultural differences of public reception and appropriation. They will discuss approaches and methods (social, cognitive and semiotics dimensions) and the conceptual frameworks that help grasp this user experience. They will learn how to design protocols to generate individual and collective qualiquantitative feedback in cultural settings, mass media and social media, in order to design more responsive and user-centered environments for content exploration.

Measuring Literature: Results, Goals, Challenges – Franco Moretti
This course is meant to assess the main novelties introduced into the study of literature – but also of film and culture in general – by the computational approach, and to investigate possible algorithmic solutions for complex aspects of literary theory and history.

Digital Musicology – Martin Rohrmeier
Digital Musicology is a vibrant field that covers the study of a wide variety of musical forms across cultures and historical traditions (e.g., from Gregorian chant up to present-day Jazz or Pop music, or Indian music), using analytical and corpus-based computational methods. DM involves bridging various sub-disciplines, such as historical musicology, music cognition, music theory, and music aesthetics.

Cultural Data Sculpting – Sarah Kenderdine
“Today we mine data: tomorrow we will sculpt it” [O’Neil, G. 2010]. Cultural data sculpting is the intersection of two dynamic entities that are transforming contemporary museology: immense and ever-expanding heterogeneous digital archives, in conjunction with their visualization in immersive, interactive display systems. To ‘sculpt cultural data’ is to bring artistic and aesthetic approaches to the problems of creating knowledge from the body of an infinite archive. Sculpting data requires us to build new modes of discovery and it demands new interfaces and interactive paradigms to support emergent narrative and embodied experience as expressions of culture heritage. This course is located at GLAM+, a new laboratory for experimentation in galleries, libraries, archives and museums. GLAM+ has a number of large scale immersive virtual reality systems including a 360-degree 3D panoramic screen and a 4K fulldome. Students will have access to a pre-existing digital archive or one of their own choice, and will be required to create novel and experimental applications for these systems.

SHS : Introduction to Project (Autumn) and Project (Spring)
SHS (Sciences Humaines et Sociales) is an interdiscplinary course over 2 semesters. The first semester (autumn) is the introduction to the project and the second semester (Spring) is the project itself. List of SHS courses to choose from.