Data mining & decision making

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

Due to the COVID-19 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).

• 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

• 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.