Study programme 2021-2022Français
Artificial Intelligence
Programme component of Master's in Computer Science à la Faculty of Science

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

Language
of instruction
Language
of assessment
HT(*) HTPE(*) HTPS(*) HR(*) HD(*) CreditsWeighting Term
  • Français
Français301500066.002nd term

AA CodeTeaching Activity (AA) HT(*) HTPE(*) HTPS(*) HR(*) HD(*) Term Weighting
S-INFO-014Artificial Intelligence3015000Q2100.00%
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. 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
S-INFO-014
  • Cours magistraux
  • Exercices dirigés

Mode of delivery

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

Required Reading

AARequired Reading
S-INFO-014Note de cours - Intelligence Artificielle - Hadrien Mélot

Required Learning Resources/Tools

AARequired Learning Resources/Tools
S-INFO-014Not applicable

Recommended Reading

AA
S-INFO-014

Recommended Learning Resources/Tools

AARecommended Learning Resources/Tools
S-INFO-014Not applicable

Other Recommended Reading

AAOther Recommended Reading
S-INFO-014- 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
S-INFO-014Authorized
(*) 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 : 17/09/2021
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