Summary

Introduction to operations research, data mining and machine learning algorithms for decision support in transportation systems.

Content

Learning Prerequisites

Required courses

Recherche opérationnelle

Learning Outcomes

By the end of the course, the student must be able to:

• Model decision processes in transportation systems as optimization problems.
• Implement and sold optimization problems using state-of-the-art solvers.
• Know aand understand various optimization approaches.
• Implement and sold optimization/data mining/machine learning problems using state-of-the-art tools and algorithms.
• Know and understand various optimization/data mining/machine learning approaches.

Teaching methods

Case-based Teaching and Problem-based Learning​

Assessment methods

• At the end of each module, each group would be required to submit a short report on a series of exercises.
• At the end of the course, both an oral and a written exams will take place:
  • At the last part of the course, each group of students will be assigned with a final project, in which they will be required to implement approaches learned during the course. Each group have to submit a report and present the project at the end of the course. The oral exam will take place during the presentation and accounts for 80% of the final grade. Assessment would be based on the quality of the report, the quality of the presentation and the answers to the questions.
  • The written exam (multiple choice questions + short answer questions) accounts for 20% of the final grade.

Resources

Ressources en bibliothèque