Study programme 2021-2022Français
Selected topics in 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-M1-SCINFO-501-MOptional UEDUPONT StéphaneF105 - Information, Signal et Intelligence artificielle
  • DUPONT Stéphane

Language
of instruction
Language
of assessment
HT(*) HTPE(*) HTPS(*) HR(*) HD(*) CreditsWeighting Term
  • Anglais, Français
Anglais, Français181800044.002nd term

AA CodeTeaching Activity (AA) HT(*) HTPE(*) HTPS(*) HR(*) HD(*) Term Weighting
S-INFO-810Selected topics in artificial intelligence1818000Q2100.00%
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.
  • Master communication techniques.
    • Communicate, both orally and in writing, their findings, original proposals, knowledge and underlying principles, in a clear, structured and justified manner.
  • Develop and integrate a high degree of autonomy.
    • Aquire new knowledge independently.
  • Apply scientific methodology.
    • Critically reflect on the impact of IT in general, and on the contribution to projects.

Learning Outcomes of UE

At the end of this UE, the student should have acquired theoretical knowledge and practical skills related to one of the major paradigms of AI: "deep learning". He/she should :
- know the major applications of artificial intelligence to natural language,
- know some of the most recent machine learning methods,
- be able to implement complex artificial neural networks
- know how to use generic software libraries for deep learning

Content of UE

The UE is composed of one learning activities that exposes:
- artificial intelligence through deep learning applied to the modeling of time sequences, and in particular to natural language processing (chatbots, machine translation, information extraction, etc.)

This learning activity will include practicals to acquire the theory.

More details about the content are provided in the ECTS sheet of the learning activity.

Prior Experience

Not applicable

Type of Assessment for UE in Q2

  • Presentation and/or works
  • Oral Examination
  • Practical test
  • Graded tests

Method of calculating the overall mark for the Q2 UE assessment

The overall rating of the UE is the rating of the unique AA that constitutes it.

- Oral exam (70%) with written preparation, covering all theory (concepts, mathematics, etc.) and practice (code of practicals, etc.) and comprising open questions and multiple choice questions.
- Reports (15%) of practicals. Score by group.
- Presentation (15%) of a tool (software module) or of a scientific article through a short written report and an oral presentation. Individual note.

Q2 UE Assessment Comments

Not applicable

Type of Assessment for UE in Q3

  • Presentation and/or works
  • Oral examination
  • Practical Test
  • Graded tests

Method of calculating the overall mark for the Q3 UE assessment

The overall rating of the UE is the rating of the unique AA that constitutes it.

- Oral exam (70%) with written preparation, covering all theory (concepts, mathematics, etc.) and practice (code of practicals, etc.) and comprising open questions and multiple choice questions.
- Reports (15%) of practicals. Score by group.
- Presentation (15%) of a tool (software module) or of a scientific article through a short written report and an oral presentation. Individual note.

Q3 UE Assessment Comments

Not applicable

Type of Teaching Activity/Activities

AAType of Teaching Activity/Activities
S-INFO-810
  • Cours magistraux
  • Travaux pratiques
  • Travaux de laboratoire
  • Projet sur ordinateur

Mode of delivery

AAMode of delivery
S-INFO-810
  • Face to face
  • From a distance
  • Mixed

Required Reading

AA
S-INFO-810

Required Learning Resources/Tools

AARequired Learning Resources/Tools
S-INFO-810All learning resources and tools required for this cours are available via Moodle, the online e-learning platform of UMONS.

Recommended Reading

AA
S-INFO-810

Recommended Learning Resources/Tools

AARecommended Learning Resources/Tools
S-INFO-810Additional recommended material is also accessible through Moodle, the online e-learning platform of UMONS.

Other Recommended Reading

AAOther Recommended Reading
S-INFO-810Not applicable

Grade Deferrals of AAs from one year to the next

AAGrade Deferrals of AAs from one year to the next
S-INFO-810Authorized
(*) 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
20, place du Parc, B7000 Mons - Belgique
Tél: +32 (0)65 373111
Courriel: info.mons@umons.ac.be