Study programme 2022-2023Français
Artificial intelligence
Programme component of Bachelor's in Engineering (MONS) (day schedule) à la Faculty of Engineering

CodeTypeHead of UE Department’s
contact details
Teacher(s)
UI-B3-IRCIVI-373-MCompulsory UEMAHMOUDI SidiF114 - Informatique, Logiciel et Intelligence artificielle
  • MAHMOUDI Sidi

Language
of instruction
Language
of assessment
HT(*) HTPE(*) HTPS(*) HR(*) HD(*) CreditsWeighting Term
  • Français
Français181800033.001st term

AA CodeTeaching Activity (AA) HT(*) HTPE(*) HTPS(*) HR(*) HD(*) Term Weighting
I-ILIA-026Artificial Intelligence1818000Q1100.00%

Programme component

Objectives of Programme's Learning Outcomes

  • Implement an engineering approach dealing with a set problem taking into account technical, economic and environmental constraints
    • Design, evaluate and optimise solutions addressing the problem
    • Identify and acquire the information and skills needed to solve the problem
  • Understand the theoretical and methodological fundamentals in science and engineering to solve problems involving these disciplines
    • Identify, describe and explain the basic principles of engineering particularly in their specialising field
    • Understand laboratory techniques: testing, measuring, monitoring protocol, and security
  • Understand the fundamentals of project management to carry out a set project, individually or as part of a team
    • Respect deadlines and timescales
  • Collaborate, work in a team
    • Interact effectively with other students to carry out collaborative projects.
  • Communicate in a structured way - both orally and in writing, in French and English - giving clear, accurate, reasoned information
    • Present analysis or experiment results in laboratory reports
  • Demonstrate thoroughness and independence throughout their studies
    • Direct their choice of modules within their degree programme in order to develop a career plan in line with the realities in the field and their profile (aspirations, strengths, weaknesses, etc.)
    • Develop their scientific curiosity and open-mindedness
    • Learn to use various resources made available to inform and train independently

Learning Outcomes of UE

UE composes of 2 AAs :
  -  I-ILIA-026 : AA "Artificial Intelligence" : AI, Machine and Deep Learning fondements 
  -  I-MARO-016 - StreamDatAnal : fondements ou tools of data processing

UE Content: description and pedagogical relevance

  -  I-ILIA-026 : AA "Artificial Intelligence"
  -  I-MARO-016 - StreamDatAnal 

Prior Experience

Not applicable

Type of Teaching Activity/Activities

AAType of Teaching Activity/Activities
I-ILIA-026
  • Cours magistraux
  • Conférences
  • Travaux pratiques
  • Projet sur ordinateur

Mode of delivery

AAMode of delivery
I-ILIA-026
  • Face-to-face

Required Learning Resources/Tools

AARequired Learning Resources/Tools
I-ILIA-026Russel, S. Et Norvig, P., (2010) Artificial Intelligence : A Modern Approach 3rd edition, Pearson

Recommended Learning Resources/Tools

AARecommended Learning Resources/Tools
I-ILIA-026Not applicable

Other Recommended Reading

AAOther Recommended Reading
I-ILIA-026Not applicable

Grade Deferrals of AAs from one year to the next

AAGrade Deferrals of AAs from one year to the next
I-ILIA-026Unauthorized

Term 1 Assessment - type

AAType(s) and mode(s) of Q1 assessment
I-ILIA-026
  • Written examination - Face-to-face
  • Production (written work, report, essay, collection, product, etc.) - To be submitted in class

Term 1 Assessment - comments

AATerm 1 Assessment - comments
I-ILIA-026Written exam
Practical exam on computer

Resit Assessment - Term 1 (B1BA1) - type

AAType(s) and mode(s) of Q1 resit assessment (BAB1)
I-ILIA-026
  • Written examination - Face-to-face
  • Production (written work, report, essay, collection, product, etc.) - To be submitted in class

Term 3 Assessment - type

AAType(s) and mode(s) of Q3 assessment
I-ILIA-026
  • Written examination - Face-to-face
  • Production (written work, report, essay, collection, product, etc.) - To be submitted in class

Term 3 Assessment - comments

AATerm 3 Assessment - comments
I-ILIA-026Idem Q1
(*) 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 dernière mise à jour de la fiche ECTS par l'enseignant : 15/05/2022
Date de dernière génération automatique de la page : 21/06/2023
20, place du Parc, B7000 Mons - Belgique
Tél: +32 (0)65 373111
Courriel: info.mons@umons.ac.be