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Algorithms for Business Intelligence and Digital Marketing

  • Teacher(s):   L.Vuillon  
  • Course given in: English
  • ECTS Credits: 3 credits
  • Schedule: Autumn Semester 2020-2021, 2.0h. course (weekly average)
  •  sessions
  • site web du cours course website
  • Related programmes:
    Master of Science (MSc) in Management, Orientation Marketing

    Maîtrise universitaire ès Sciences en management, Orientation Behaviour, Economics and Evolution

    Master of Science (MSc) in Management, Orientation Business Analytics

    Master of Science (MSc) in Management, Orientation Strategy, Organization and Leadership

 

Objectives

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.

Contents

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.

References

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

First attempt

Exam:
Written 2h00 hours
Documentation:
Allowed
Calculator:
Allowed
Evaluation:

2-hour written exam. Online exam

Retake

Exam:
Written 2h00 hours
Documentation:
Allowed
Calculator:
Allowed
Evaluation:

2-hour written exam. Online exam.



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