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Algorithms for Business Intelligence and Digital Marketing

  • Enseignant(s):   L.Vuillon  
  • Titre en français: Algorithmes pour la BI et le marketing digital
  • Cours donné en: anglais
  • Crédits ECTS: 3 crédits
  • Horaire: Semestre d'automne 2020-2021, 2.0h. de cours (moyenne hebdomadaire)
  •  séances
  • site web du cours site web du cours
  • Formations concernées:
    Maîtrise universitaire ès Sciences en management, Orientation business analytics

    Maîtrise universitaire ès Sciences en management, Orientation stratégie, organisation et leadership

    Maîtrise universitaire ès Sciences en management, Orientation marketing

    Maîtrise universitaire ès Sciences en management, Orientation comportement, économie et évolution

 

Objectifs

The master course « Algorithms for Business Intelligence and Digital Marketing » will focus on important algorithms for BI. We will go beyond the usual « black box » presentation of these modern methods. Indeed, it is crucial to have a global vision of these methods to make, for example, the link between the Marketing division and the IT division of a company. The goal will consist of having the minimal vocabulary and the conceptual understanding to be able to talk of Marketing Algorithms with data scientists and computer scientists. Thus, we will study carefully various classes of algorithms and understand the mathematical concepts behind them and explain how to use these algorithms in BI and Massive Data context.

Contenus

We will learn how to construct efficient algorithms for the following topics:

  • Graph algorithms and optimization;
  • Recommendation systems for digital marketing;
  • Clustering methods and data visualization;
  • Dimension reduction and mapping;
  • Data mining and editing distance;
  • Approximation algorithms for the travelling salesman problem;
  • Randomized algorithms and parallel algorithms.

Références

Cormen, T. H., Leiserson, C. E., Rivest, R. L., & Stein, C. (2009). Introduction to algorithms. MIT press.

Leskovec, J., Rajaraman, A., & Ullman, J. D. (2014). Mining of massive datasets. Cambridge university press.

Murphy, K. P. (2012). Machine learning: a probabilistic perspective. MIT press.

Vercellis, C. (2009). Business intelligence: data mining and optimization for decision making. New York: Wiley.

Evaluation

1ère tentative

Examen:
Ecrit 2h00 heures
Documentation:
Autorisée
Calculatrice:
Non autorisée
Evaluation:

2-hour written exam.

Rattrapage

Examen:
Ecrit 2h00 heures
Documentation:
Autorisée
Calculatrice:
Non autorisée
Evaluation:

2-hour written exam.



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