Blondel Vincent; Delvenne Jean-Charles coordinator; Krings Gautier (compensates Blondel Vincent); Peel Leto (compensates Delvenne Jean-Charles);
Some familiarity with linear algebra and discrete mathematics is required (such as given in LFSAB1101, LFSAB1102, LINMA1691).
The course explores questions, mainly of an algorithmic nature, regarding the challenges offered by the emergence of Big Data.
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”.
At the end of this learning unit, the student is able to :
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 course contents may vary from one year to another and can tackle various algorithmic questions related to storage, broadcast or analysis of massive datasets (Big Data). E.g., plagiarism detection, web pages ranking, frequent patterns detection, social networks analysis, parallel computing and storage, principles of peer-to-peer networks, etc.
In part ex cathedra, and in part presented by the students themselves based on a book chapter or other documents.
Oral exam with written preparation. Oral and written presentation of theory and real data analysis during the term.
Title of the programme
Master  in Data Science Engineering
Master  in Computer Science and Engineering
Master  in Agricultural Bioengineering
Master  in Environmental Bioengineering
Master  in Statistic: General
Master  in Computer Science
Master  in Mathematical Engineering
Master  in data Science: Statistic
Master  in data Science: Information technology