Data mining & decision making [ LSINF2275 ]
5.0 crédits ECTS
30.0 h + 30.0 h
2q
Teacher(s) |
Saerens Marco ;
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Language |
English
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Place of the course |
Louvain-la-Neuve
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Online resources |
> https://icampus.uclouvain.be/claroline/course/index.php?cid=sinf2275
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Main themes |
The course is structured around four themes
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Complements of data mining,
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Decision making,
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Information retrieval,
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Link analysis and web/graph mining .
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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:
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INFO1.1-3
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INFO2.2-3
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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:
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SINF1.M4
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SINF2.2-3
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SINF5.2
Students completing this course successfully will be able to
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explain quantitative and qualitative data mining methods and to apply them to decision making
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develop a critical view of data mining techniques in specific application domains
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master information retrieval techniques from very large data collection, possibly enriched with link structures (WEB, social networks, ...)
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explain application of information retrieval techniques in the context of search engines and automated recommendation systems
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implement data mining and information retrieval algorithms within standard software environments such as S-Plus, R, SAS, Weka or Matlab
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Content |
Complements of data mining
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Canonical correlation analysis
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Correspondence analysis
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Partial least squares regression
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Log-linear models
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Association rules
Decision making
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Markov decision processes and reinforcement learning
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Exploration/exploitation and bandit problems
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Utility theory
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Multi-criteria preference modeling - the Promethee method
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Probabilistic reasoning with bayesian networks
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Possibility theory
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Two-players game theory
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Collective decisions
Information retrieval
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The basic vector-space model
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The probabilistic model
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Ranking web pages :PageRank, HITS, etc.
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Collaborative recommendation models (recommender systems) .
Link analysis and web/graph mining
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Network community detection
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Similarity measures between nodes
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Spectral graph partitioning and mapping
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Reputation models
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Bibliography |
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Alpaydin (2004), "Introduction to machine learning". MIT Press.
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Bardos (2001), "Analyse discriminante. Application au risque et scoring financier. Dunod.
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Bishop (1995), "Neural networks for pattern recognition". Clarendon Press.
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Bishop (2006), "Pattern recognition and machine learning". Springer-Verlag.
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Bouroche & Saporta (1983), "L'analyse des données". Que Sais-je.
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Cornuéjols & Miclet (2002), "Apprentissage artificiel. Concepts et algorithmes". Eyrolles.
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Duda, Hart & Stork (2001), "Pattern classification, 2nd ed". John Wiley & Sons.
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Dunham (2003), "Data mining. Introductory and advanced topics". Prentice-Hall.
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Greenacre (1984), "Theory and applications of correspondence analysis". Academic Press.
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Han & Kamber (2005), "Data mining: Concepts and techniques, 2nd ed.". Morgan Kaufmann.
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Hand (1981), "Discrimination and classification". John Wiley & Sons.
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Hardle & Simar (2003), "Applied multivariate statistical analysis". Springer-Verlag. Disponible à http://www.quantlet.com/mdstat/scripts/mva/htmlbook/mvahtml.html
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Hastie, Tibshirani & Friedman (2001), "The elements of statistical learning". Springer-Verlag.
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Johnson & Wichern (2002), "Applied multivariate statistical analysis, 5th ed". Prentice-Hall.
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Lebart, Morineau & Piron (1995), "Statistique exploratoire multidimensionnelle". Dunod.
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Mitchell (1997), "Machine learning". McGraw-Hill.
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Naim, Wuillemin, Leray, Pourret & Becker (2004), "Réseaux bayesiens". Editions Eyrolles.
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Nilsson (1998), "Artificial intelligence: A new synthesis". Morgan Kaufmann.
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Ripley (1996), "Pattern recognition and neural networks". Cambridge University Press.
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Rosner (1995), "Fundamentals of biostatistics, 4th ed".Wadsworth Publishing Company.
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Saporta (1990), "Probabilités, analyse des données et statistique". Editions Technip.
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Tan, Steinbach & Kumer (2005), "Introduction to data mining". Pearson.
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Theodoridis & Koutroumbas (2003), "Pattern recognition, 3th ed". Academic Press.
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Therrien (1989), "Decision, estimation and classification". Wiley & Sons.
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Venables & Ripley (2002), "Modern applied statistics with S. Springer-Verlag.
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Webb (2002), "Statistical pattern recognition, 2nd ed". John Wiley and Sons.
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Other information |
Background :
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LBIR1304 ou LFSAB1105 : a course on probability theory and mathematical statistics,
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LBIR1200 ou LFSAB1101 : an undergraduate course on matrix algebra
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LFSAB1402 : a course on the basis of programming
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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
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Faculty or entity in charge |
> INFO
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