Due to the COVID19 crisis, the information below is subject to change,
in particular that concerning the teaching mode (presential, distance or in a comodal or hybrid format).
Teacher(s)
Language
English
Main themes
The course is structured around four themes
 Complements of data mining,
 Decision making,
 Information retrieval,
 Link analysis and web/graph mining .
Aims
At the end of this learning unit, the student is able to :  
1 
Given the learning outcomes of the "Master in Computer Science and Engineering" program, this course contributes to the development, acquisition and evaluation of the following learning outcomes:

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 content changes from year to year, but the chapters with a * are always teached.
* Complements of data mining
* Complements of data mining
 Principal components analysis
 Canonical correlation analysis
 Correspondence analysis
 Loglinear models
 Discriminant analysis
 Multidimensional scaling
 Markov and hidden Markov models
 etc
 Dynamic programming and applications
 Markov decision processes and reinforcement learning
 Exploration/exploitation and bandit problems
 Utility theory
 Multicriteria preference modeling  the Promethee method
 Probabilistic reasoning with bayesian networks
 Twoplayers game theory
 Collective decisions
 The basic vectorspace model
 The probabilistic model
 Ranking web pages : PageRank, HITS, etc.
 Collaborative recommendation models (recommender systems) .
 Network community detection
 Similarity measures between nodes
 Spectral graph partitioning and mapping
Evaluation methods
Due to the COVID19 crisis, the information in this section is particularly likely to change.
 One or two projects for 6 points on 20 to 10 points on 20 (for both projects), depending on the size and the number of these projects. This will be specified at the first or second lecture.
 Oral or written exam (depending on the health situation and the number of students) : 14/20 to 10/20 (depending on the scenario concerning the projects).
Other information
Background :
 LBIR1304 ou LFSAB1105 : a course on probability theory and mathematical statistics,
 LBIR1200 ou LFSAB1101 : an undergraduate course on matrix algebra,
 LFSAB1402 : a course on the basis of programming
Online resources
Available on Moodle
Bibliography
Some recommended reference books :
 Alpaydin (2004), "Introduction to machine learning". MIT Press.
 Bardos (2001), "Analyse discriminante. Application au risque et scoring financier. Dunod.
 Bishop (1995), "Neural networks for pattern recognition". Clarendon Press.
 Bishop (2006), "Pattern recognition and machine learning". SpringerVerlag.
 Bouroche & Saporta (1983), "L'analyse des données". Que Saisje.
 Cornuéjols & Miclet (2002), "Apprentissage artificiel. Concepts et algorithmes". Eyrolles.
 Duda, Hart & Stork (2001), "Pattern classification, 2nd ed". John Wiley & Sons.
 Dunham (2003), "Data mining. Introductory and advanced topics". PrenticeHall.
 Greenacre (1984), "Theory and applications of correspondence analysis". Academic Press.
 Han & Kamber (2005), "Data mining: Concepts and techniques, 2nd ed.". Morgan Kaufmann.
 Hand (1981), "Discrimination and classification". John Wiley & Sons.
 Hardle & Simar (2003), "Applied multivariate statistical analysis". SpringerVerlag. Disponible à http://www.quantlet.com/mdstat/scripts/mva/htmlbook/mvahtml.html
 Hastie, Tibshirani & Friedman (2001), "The elements of statistical learning". SpringerVerlag.
 Johnson & Wichern (2002), "Applied multivariate statistical analysis, 5th ed". PrenticeHall.
 Lebart, Morineau & Piron (1995), "Statistique exploratoire multidimensionnelle". Dunod.
 Mitchell (1997), "Machine learning". McGrawHill.
 Naim, Wuillemin, Leray, Pourret & Becker (2004), "Réseaux bayesiens". Editions Eyrolles.
 Nilsson (1998), "Artificial intelligence: A new synthesis". Morgan Kaufmann.
 Ripley (1996), "Pattern recognition and neural networks". Cambridge University Press.
 Rosner (1995), "Fundamentals of biostatistics, 4th ed".Wadsworth Publishing Company.
 Saporta (1990), "Probabilités, analyse des données et statistique". Editions Technip.
 Tan, Steinbach & Kumer (2005), "Introduction to data mining". Pearson.
 Theodoridis & Koutroumbas (2003), "Pattern recognition, 3th ed". Academic Press.
 Therrien (1989), "Decision, estimation and classification". Wiley & Sons.
 Venables & Ripley (2002), "Modern applied statistics with S. SpringerVerlag.
 Webb (2002), "Statistical pattern recognition, 2nd ed". John Wiley and Sons.
Faculty or entity
Force majeure
Evaluation methods
Provided the health situation, it could be that the final exam has to be organized remotely and that its form changes from one exam session to another, during the same academic year. In that case, the method of assessment will take the form of a written or a multiplechoice exam (possibly with proctoring via Teams or another software), or even a remote oral exam if the number of students is not too large. These modalities will be communicated to the students within the official time frame provided by the university.
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 Computer Science and Engineering
Master [120] in Computer Science
Certificat d'université : Statistique et sciences des données (15/30 crédits)
Master [120] in Actuarial Science
Master [120] in Mathematical Engineering
Master [120] in Agricultural Bioengineering
Master [120] in Forests and Natural Areas Engineering
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