Algorithms in data science

linma2472  2020-2021  Louvain-la-Neuve

Algorithms in data science
Due to the COVID-19 crisis, the information below is subject to change, in particular that concerning the teaching mode (presential, distance or in a comodal or hybrid format).
5 credits
30.0 h + 22.5 h
Q1
Teacher(s)
Blondel Vincent; Delvenne Jean-Charles (coordinator); Krings Gautier (compensates Blondel Vincent);
Language
English
Prerequisites
Some familiarity with linear algebra and discrete mathematics is required (such as given in LFSAB1101, LFSAB1102, LINMA1691).
Main themes
The course explores questions, mainly of an algorithmic nature, regarding the challenges offered by the emergence of Big Data.
Aims

At the end of this learning unit, the student is able to :

1 Learning outcomes :
  • AA1 : 1,2,3
  • AA3 : 1,3
  • AA4 : 1
  • AA5 : 1,2,3, 5,6
More specifically, at the end of the course the student will be able to :
  • read a general or specialized literature on a specific cutting-edge theme of discrete mathematics, and summarize the key messages and results
  • explain those messages to their peers in a clear and precise way
  • solve mathematical problems in application to those results
  • identify the possible caveats of those results and criticize the exposition chosen by the references
  • relate the concepts encountered in the literature to concepts covered in other course, despite different notations or viewpoints
The mathematical objectives can change from year to year.
 

The contribution of this Teaching Unit to the development and command of the skills and learning outcomes of the programme(s) can be accessed at the end of this sheet, in the section entitled “Programmes/courses offering this Teaching Unit”.
Content
The course contents may vary from one year to another and can tackle various algorithmic questions related to analysis, storage, or broadcast of large datasets. E.g., data anonymisation, plagiarism detection, social networks analysis, principles of peer-to-peer networks, etc.
Teaching methods

Due to the COVID-19 crisis, the information in this section is particularly likely to change.

In part ex cathedra lectures, and in part  projects with written and/or oral reports.
Evaluation methods

Due to the COVID-19 crisis, the information in this section is particularly likely to change.

Oral and written presentation of theory and/or real data analysis during the term. Written or oral exam.
Bibliography
Variable.
Teaching materials
  • Documents sur la page Moodle / Documents on the Moodle page
Faculty or entity
Force majeure
Evaluation methods
The exam is written, on site. An exam of adapted form will be proposed to the students with a valid quarantine certificate or a 'formulaire retour' from the Foreign Office, if the teachers (Gautier Krings and Jean-Charles Delvenne) are warned asap and in any case before the main exam. This alternative exam will cover the same topics as the main exam, and will be organised in a form compatible with the situation of the student.


Programmes / formations proposant cette unité d'enseignement (UE)

Title of the programme
Sigle
Credits
Prerequisites
Aims
Master [120] in Data Science : Statistic

Master [120] in Mathematics

Master [120] in Computer Science and Engineering

Master [120] in Computer Science

Master [120] in Mathematical Engineering

Master [120] in Agricultural Bioengineering

Master [120] in Environmental Bioengineering

Master [120] in Data Science Engineering

Master [120] in Chemistry and Bioindustries

Master [120] in Data Science: Information Technology

Master [120] in Statistic: General