Data mining & decision making

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

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 .

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

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”.

• Two projects : 3/20 each
• Oral exam : 14/20

Complements of data mining

• Canonical correlation analysis
• Correspondence analysis
• Log-linear models
• Discriminant analysis
• Multidimensional scaling
• Markov and hidden Markov models
• etc

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
• 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

Course material  : teacher slides and recommanded 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". 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.

• 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