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Computational Complexity II

  • Teacher(s):   L.Vuillon  
  • Course given in: English
  • ECTS Credits: 3 credits
  • Schedule: Autumn Semester 2019-2020, 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 ``Computational Complexity II” will focus on important algorithms for Business Intelligence and Digital Marketing. We will evaluate the computational complexity and understand the mathematical concepts behind each algorithm.

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.

Pre-requisites

Computational Complexity I

Evaluation

First attempt

Exam:
Written 2h00 hours
Documentation:
Allowed
Calculator:
Not allowed
Evaluation:

2-hour written exam.

Retake

Exam:
Written 2h00 hours
Documentation:
Allowed
Calculator:
Not allowed
Evaluation:

2-hour written exam.



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