Note from June 29, 2020
Although we do not yet know how long the social distancing related to the Covid-19 pandemic will last, and regardless of the changes that had to be made in the evaluation of the June 2020 session in relation to what is provided for in this learning unit description, new learnig unit evaluation methods may still be adopted by the teachers; details of these methods have been - or will be - communicated to the students by the teachers, as soon as possible.
Although we do not yet know how long the social distancing related to the Covid-19 pandemic will last, and regardless of the changes that had to be made in the evaluation of the June 2020 session in relation to what is provided for in this learning unit description, new learnig unit evaluation methods may still be adopted by the teachers; details of these methods have been - or will be - communicated to the students by the teachers, as soon as possible.
5 credits
30.0 h + 15.0 h
Q2
Teacher(s)
Saerens Marco;
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
- Log-linear 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
- Multi-criteria preference modeling - the Promethee method
- Probabilistic reasoning with bayesian networks
- Two-players game theory
- Collective decisions
- The basic vector-space 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
- Two projects for 6 points on 20 or 7 points on 20 (for the two projects), depending on the size of these projects and the year
- Oral exam : 14/20 or 13/20
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
Bibliography
Support de cours : transparents de l'enseignant et lectures recommandées :
- 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". Springer-Verlag.
- Bouroche & Saporta (1983), "L'analyse des données". Que Sais-je.
- 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". Prentice-Hall.
- 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". Springer-Verlag. Disponible à http://www.quantlet.com/mdstat/scripts/mva/htmlbook/mvahtml.html
- Hastie, Tibshirani & Friedman (2001), "The elements of statistical learning". Springer-Verlag.
- Johnson & Wichern (2002), "Applied multivariate statistical analysis, 5th ed". Prentice-Hall.
- Lebart, Morineau & Piron (1995), "Statistique exploratoire multidimensionnelle". Dunod.
- Mitchell (1997), "Machine learning". McGraw-Hill.
- 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. Springer-Verlag.
- Webb (2002), "Statistical pattern recognition, 2nd ed". John Wiley and Sons.
Faculty or entity
INFO
Programmes / formations proposant cette unité d'enseignement (UE)
Title of the programme
Sigle
Credits
Prerequisites
Aims
Master [120] in Forests and Natural Areas Engineering
Master [120] in Chemistry and Bioindustries
Master [120] in Agricultural Bioengineering
Master [120] in Mathematical Engineering
Master [120] in Computer Science and Engineering
Master [120] in Data Science : Statistic
Master [120] in Computer Science
Master [120] in Data Science Engineering
Certificat d'université : Statistique et sciences des données (15/30 crédits)
Master [120] in Data Science: Information Technology
Master [120] in Actuarial Science
Master [120] in Statistic: General
Master [120] in Environmental Bioengineering