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

CodeTypeHead of UE Department’s
contact details
Teacher(s)
US-M1-SCINFO-002-MOptional UEMELOT HadrienS825 - Algorithmique
  • MELOT Hadrien

Language
of instruction
Language
of assessment
HT(*) HTPE(*) HTPS(*) HR(*) HD(*) CreditsWeighting Term
  • Français
Français201000033.001st term

AA CodeTeaching Activity (AA) HT(*) HTPE(*) HTPS(*) HR(*) HD(*) Term Weighting
S-INFO-021Artificial Intelligence and graphs2010000Q1100.00%
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 is that students deepen some classical topics 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

Follow with success the course "Intelligence Artificielle".

Type of Assessment for UE in Q1

  • Oral examination

Q1 UE Assessment Comments

Oral exam 100%

Type of Assessment for UE in Q2

  • Oral Examination

Q2 UE Assessment Comments

Oral exam 100%

Type of Assessment for UE in Q3

  • Oral examination

Q3 UE Assessment Comments

Oral examination 100%

Type of Resit Assessment for UE in Q1 (BAB1)

  • N/A

Q1 UE Resit Assessment Comments (BAB1)

Not applicable.

Type of Teaching Activity/Activities

AAType of Teaching Activity/Activities
S-INFO-021
  • Cours magistraux
  • Exercices dirigés

Mode of delivery

AAMode of delivery
S-INFO-021
  • Face to face

Required Reading

AA
S-INFO-021

Required Learning Resources/Tools

AARequired Learning Resources/Tools
S-INFO-021Not applicable

Recommended Reading

AA
S-INFO-021

Recommended Learning Resources/Tools

AARecommended Learning Resources/Tools
S-INFO-021Not applicable

Other Recommended Reading

AAOther Recommended Reading
S-INFO-021Russel, S. and Norvig, P., Artificial Intelligence: A Modern Approach, 3ième édition, Pearson, 2010   Williamson, Shmoys, The Design of Approximation Algorithms, Cambridge University Press (2011). Electronic version available online: www.designofapproxalgs.com

Grade Deferrals of AAs from one year to the next

AAGrade Deferrals of AAs from one year to the next
S-INFO-021Authorized
(*) 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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