Study programme 2019-2020Français
Artificial Intelligence
Programme component of Master's in Computer Engineering and Management (Charleroi (Hor. décalé)) à la Faculty of Engineering

Students are asked to consult the ECTS course descriptions for each learning activity (AA) to know what assessment methods are planned for the end of Q3

CodeTypeHead of UE Department’s
contact details
UI-M1-IRIGIG-846-COptional UEMELOT HadrienS825 - Algorithmique
  • MELOT Hadrien

of instruction
of assessment
HT(*) HTPE(*) HTPS(*) HR(*) HD(*) CreditsWeighting Term
  • Français
Français301500055.002nd term

AA CodeTeaching Activity (AA) HT(*) HTPE(*) HTPS(*) HR(*) HD(*) Term Weighting
S-INFO-061Artificial Intelligence3015000Q2100.00%
Programme component

Objectives of Programme's Learning Outcomes

  • Imagine, design, develop, and implement conceptual models and computer solutions to address complex problems including decision-making, optimisation, management and production as part of a business innovation approach by integrating changing needs, contexts and issues (technical, economic, societal, ethical and environmental).
    • On the basis of modelling, design a system or a strategy addressing the problem raised; evaluate them in light of various parameters of the specifications.
  • Mobilise a structured set of scientific knowledge and skills and specialised techniques in order to carry out computer and management engineering missions, using their expertise and adaptability.
    • Master and appropriately mobilise knowledge, models, methods and techniques specific to computer management engineering.
    • Analyse and model an innovative IT solution or a business strategy by critically selecting theories and methodological approaches (modelling, optimisation, algorithms, calculations), and taking into account multidisciplinary aspects.

Learning Outcomes of UE

The goal of this course if to initiate the students to classical fields of Artificial Intelligence. The students will be able to identify when a particular method can be applied. The course will concentrate on algorithmic aspect of Artificial Intelligence.

Content of UE

See unique learning activity.

Prior Experience

Knowledge of a programming language (e.g., Python ou Java) and of basic data structures (lists, trees, graphs).

Type of Assessment for UE in Q2

  • Written examination
  • Graded tests

Q2 UE Assessment Comments

Written exam 85%
Exercices 15%

Type of Assessment for UE in Q3

  • Oral examination

Q3 UE Assessment Comments

Oral exam 100%

Type of Teaching Activity/Activities

AAType of Teaching Activity/Activities
  • Cours magistraux
  • Travaux pratiques

Mode of delivery

AAMode of delivery
  • Mixed

Required Reading

AARequired Reading
S-INFO-061Note de cours - Intelligence Artificielle - Hadrien Mélot

Required Learning Resources/Tools

AARequired Learning Resources/Tools
S-INFO-061Not applicable

Recommended Reading


Recommended Learning Resources/Tools

AARecommended Learning Resources/Tools
S-INFO-061Not applicable

Other Recommended Reading

AAOther Recommended Reading
S-INFO-061- Russel, S. and Norvig, P., Artificial Intelligence: A Modern Approach, 3ième édition, Pearson, 2010
- Talbi, E.-G., Metaheuristics: from design to implementation, Wiley, 2009

Grade Deferrals of AAs from one year to the next

AAGrade Deferrals of AAs from one year to the next
(*) HT : Hours of theory - HTPE : Hours of in-class exercices - HTPS : hours of practical work - HD : HMiscellaneous time - HR : Hours of remedial classes. - Per. (Period), Y=Year, Q1=1st term et Q2=2nd term
Date de génération : 13/07/2020
20, place du Parc, B7000 Mons - Belgique
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