Study programme 2021-2022Français
Advanced 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-502-MOptional UEDUPONT StéphaneF105 - Information, Signal et Intelligence artificielle
  • DUPONT Stéphane

Language
of instruction
Language
of assessment
HT(*) HTPE(*) HTPS(*) HR(*) HD(*) CreditsWeighting Term
  • Anglais
Anglais121200033.002nd term

AA CodeTeaching Activity (AA) HT(*) HTPE(*) HTPS(*) HR(*) HD(*) Term Weighting
I-ILIA-027Advanced topics in Artificial Intelligence1212000Q2100.00%
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: probabilistic models. He/she should :
- understand the importance and interest of the probabilistic perspective in AI.
- know the basic theory of probabilistic graphical models and Bayesian networks.
- know the hidden Markov models, particle filters, mixtures of Gaussians, and the latent Dirichlet allocation.
- be able to put into practice statistical inference approaches.
- know how to use software libraries dedicated to probabilistic programming (PyMC3, Pyro, etc.).

Content of UE

The UE is composed of one learning activities that exposes:
- artificial intelligence relying on probability theory, based on probabilistic graphical models, Bayesian networks, and their implementation through probabilistic programming.

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 (65%) 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 (20%) 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 (65%) 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 (20%) 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
I-ILIA-027
  • Cours magistraux
  • Travaux pratiques
  • Travaux de laboratoire
  • Projet sur ordinateur

Mode of delivery

AAMode of delivery
I-ILIA-027
  • Face to face
  • From a distance
  • Mixed

Required Reading

AA
I-ILIA-027

Required Learning Resources/Tools

AARequired Learning Resources/Tools
I-ILIA-027All learning resources and tools required for this cours are available via Moodle, the online e-learning platform of UMONS.

Recommended Reading

AA
I-ILIA-027

Recommended Learning Resources/Tools

AARecommended Learning Resources/Tools
I-ILIA-027Additional recommended material is also accessible through Moodle, the online e-learning platform of UMONS.

Other Recommended Reading

AAOther Recommended Reading
I-ILIA-027Not applicable

Grade Deferrals of AAs from one year to the next

AAGrade Deferrals of AAs from one year to the next
I-ILIA-027Unauthorized
(*) 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