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Data mining & decision making [ LSINF2275 ]


5.0 crédits ECTS  30.0 h + 30.0 h   2q 

Teacher(s) Saerens Marco ;
Language English
Place
of the course
Louvain-la-Neuve
Online resources

> https://icampus.uclouvain.be/claroline/course/index.php?cid=sinf2275

Main themes

The course is structured around four themes

  1. Complements of data mining,
  2. Decision making,
  3. Information retrieval,
  4. Link analysis and web/graph mining .
Aims

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:

  • INFO1.1-3
  • INFO2.2-3
  • INFO5.2

Given the learning outcomes of the "Master [120] in Computer Science" program, this course contributes to the development, acquisition and evaluation of the following learning outcomes:

  • SINF1.M4
  • SINF2.2-3
  • SINF5.2

Students completing this course successfully will be able to

  • explain quantitative and qualitative data mining methods and to apply them to decision making
  • develop a critical view of data mining techniques in specific application domains
  • master information retrieval techniques from very large data collection, possibly enriched with link structures (WEB, social networks, ...)
  • explain application of information retrieval techniques in the context of search engines and automated recommendation systems
  • implement data mining and information retrieval algorithms within standard software environments such as S-Plus, R, SAS, Weka or Matlab
Content

Complements of data mining

  • Canonical correlation analysis
  • Correspondence analysis
  • Partial least squares regression
  • Log-linear models
  • Association rules

Decision making

  • 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
  • Possibility theory
  • Two-players game theory
  • Collective decisions

Information retrieval

  • The basic vector-space model
  • The probabilistic model
  • Ranking web pages :PageRank, HITS, etc.
  • Collaborative recommendation models (recommender systems) .

Link analysis and web/graph mining

  • Network community detection
  • Similarity measures between nodes
  • Spectral graph partitioning and mapping
  • Reputation models
Bibliography
  • 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.
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
Cycle et année
d'étude
> Master [120] in Statistics: General
> Master [120] in Business engineering
> Master [120] in Agricultural Bioengineering
> Master [120] in Environmental Bioengineering
> Master [120] in Forests and Natural Areas Engineering
> Master [120] in Chemistry and Bio-industries
> Master [120] in Computer Science
> Master [120] in Computer Science and Engineering
> Certificat universitaire en statistique
> Master [120] in Business Engineering
Faculty or entity
in charge
> INFO


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