Study programme 2023-2024Français
Artificial Intelligence
Programme component of Master's in Computer Science (MONS) (day schedule) à la Faculty of Science

CodeTypeHead of UE Department’s
contact details 		Teacher(s)
US-MC-SCINFO-043-MCompulsory UEMELOT HadrienS825 - Algorithmique
  • MELOT Hadrien
  • DUPONT Stéphane

Language
of instruction		Language
of assessment		 HT(*) HTPE(*) HTPS(*) HR(*) HD(*) CreditsWeighting Term
  • Français
Français302000066.001st term

AA CodeTeaching Activity (AA) HT(*) HTPE(*) HTPS(*) HR(*) HD(*) Term Weighting
S-INFO-014Artificial Intelligence classical approaches2212000Q166.00%
S-INFO-114Introduction to Deep Learning88000Q134.00%

Programme component

Objectives of Programme's Learning Outcomes

  • Have acquired highly specialised and integrated knowledge and broad skills in the various disciplines of computer science, which come after those within the Bachelor's in computer science.

Learning Outcomes of UE

The goal of this course if to initiate the students to classical fields of Artificial Intelligence and Deep Learning. The students will be able to identify when a particular method can be applied.

UE Content: description and pedagogical relevance

See both learning activities.

Prior Experience

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

Type of Teaching Activity/Activities

AAType of Teaching Activity/Activities
S-INFO-014
  • Cours magistraux
  • Exercices dirigés
S-INFO-114
  • Cours magistraux
  • Exercices dirigés
  • Utilisation de logiciels

Mode of delivery

AAMode of delivery
S-INFO-014
  • Face-to-face
S-INFO-114
  • Face-to-face

Required Reading

AARequired Reading
S-INFO-014Note de cours - Approches Classiques de l'Intelligence Artificielle - Hadrien Mélot#newline#
S-INFO-114

Required Learning Resources/Tools

AARequired Learning Resources/Tools
S-INFO-014Not applicable
S-INFO-114All learning resources and tools required for this cours are available via Moodle, the online e-learning platform of UMONS.

Recommended Learning Resources/Tools

AARecommended Learning Resources/Tools
S-INFO-014Not applicable
S-INFO-114Additional recommended material is also accessible through Moodle, the online e-learning platform of UMONS.

Other Recommended Reading

AAOther Recommended Reading
S-INFO-014- Russel, S. and Norvig, P., Artificial Intelligence: A Modern Approach, 3ième édition, Pearson, 2010#newline# - Talbi, E.-G., Metaheuristics: from design to implementation, Wiley, 2009
S-INFO-114Not applicable

Grade Deferrals of AAs from one year to the next

AAGrade Deferrals of AAs from one year to the next
S-INFO-014Authorized
S-INFO-114Authorized

Term 1 Assessment - type

AAType(s) and mode(s) of Q1 assessment
S-INFO-014
  • Written examination - Face-to-face
  • Graded assignment(s) - Remote
S-INFO-114
  • Written examination - Face-to-face

Term 1 Assessment - comments

AATerm 1 Assessment - comments
S-INFO-014Written exam (85%) and online assignements (15%)
S-INFO-114Cfr. types and modes of assessment

Resit Assessment - Term 1 (B1BA1) - type

AAType(s) and mode(s) of Q1 resit assessment (BAB1)
S-INFO-014
  • N/A - Néant
S-INFO-114
  • N/A - Néant

Term 3 Assessment - type

AAType(s) and mode(s) of Q3 assessment
S-INFO-014
  • Oral examination - Face-to-face
S-INFO-114
  • Written examination - Face-to-face

Term 3 Assessment - comments

AATerm 3 Assessment - comments
S-INFO-014Oral exam 100%
S-INFO-114Cfr. types and modes of assessment
(*) 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
More information on Higher Education Structure (’Paysage’ Decree)
Date de dernière mise à jour de la fiche ECTS par l'enseignant : 03/05/2023
Date de dernière génération automatique de la page : 31/05/2025
20, place du Parc, B7000 Mons - Belgique
Tél: +32 (0)65 373111
Courriel: info.mons@umons.ac.be