Learning outcomes

After this course, the student will be able to

  •  describe various knowledge models and explain the associated learning and solving algorithms
  • describe their involvement in case studies and DSS implementations
  • draw a scientific litterature review and write a synthesis of a new research question
  • communicate the synthesis by both writen and oral reporting accordig to scientific standards

Content

The lectures present advanced artificial intelligence and machine learning techniques and sudy their implementation in business DSS.

Teaching methods

Learning is based on presentations and reading of scientif book and papers

Assessment method

Evaluation involves three aspects :

  • active participation to the lectures,
  • reading report and oral presentation  
  • oral exam

Active participation to lectures is object of a continuous evaluation.  If a student does not contribute, he/she is not admissible to evaluation (art.36 and 38 REE)

Oral exam is only accessible to the students that have participated to the lectures and presented the report (art. 38 of REE)

All details on evaluation will be presented at the first lecture and on the webcampus.

Sources, references and any support material

Students are advised to obtain

  • Natural Computing Algorithm : Anthony Brabazon, Michael O'Neill, Seán McGarraghy


They will usefully consult:

  • Artificial Intelligence : A modern approach, Stuart Russell and Peter Norvig
  • Machine Learning, Tom Mitchell,
  • Introduction to Machine Learning, Ethem Alpaydin

 

Language of instruction

English
Training Study programme Block Credits Mandatory
Master in Business Engineering Finalité spécialisée en data science 1 5 Yes
Master in Business Engineering Finalité spécialisée en Sustainable & Digital Management 1 5 Yes
Master in Business Engineering Finalité spécialisée en data science 2 5 Yes
Master in Business Engineering Finalité spécialisée en Sustainable & Digital Management 2 5 Yes