Study programme 2021-2022Français
Advanced machine learning and deep learning
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-060-MOptional UESIEBERT XavierF151 - Mathématique et Recherche opérationnelle
  • SIEBERT Xavier
  • MAHMOUDI Sidi

Language
of instruction
Language
of assessment
HT(*) HTPE(*) HTPS(*) HR(*) HD(*) CreditsWeighting Term
  • Anglais
Anglais303000055.001st term

AA CodeTeaching Activity (AA) HT(*) HTPE(*) HTPS(*) HR(*) HD(*) Term Weighting
I-MARO-202Advanced Machine Learning2424000Q180.00%
I-ILIA-202Advanced Deep Learning66000Q120.00%
Programme component

Objectives of Programme's Learning Outcomes

  • Manage large-scale software development projects.
    • Apply, mobilise, articulate and promote the knowledge and skills acquired in order to help lead and complete a project.
    • Demonstrate independence and their ability to work alone or in teams.
  • Manage research, development and innovation.
    • Understand unprecedented problems in computer science and its applications.
    • Organise and lead a research, development or innovation project to completion.
  • 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.
    • Pursue further training and develop new skills independently.
  • Apply scientific methodology.
    • Critically reflect on the impact of IT in general, and on the contribution to projects.
    • Demonstrate thoroughness, independence, creativity, intellectual honesty, and ethical values.

Learning Outcomes of UE

Get familiar with the contemporary methods in machine learning (active learning, reinforcement learning, depp networks) Study these methods within the frameworks of statistical learning theory

Content of UE

active learning, reinforcement learning, deep network, statistical learning theory

Prior Experience

Not applicable

Type of Assessment for UE in Q1

  • Presentation and/or works
  • Written examination

Q1 UE Assessment Comments

personal work + written exam

Type of Assessment for UE in Q2

  • Presentation and/or works
  • Written examination

Q2 UE Assessment Comments

Theoretical exam and presentation of a practical project on the computer  

Type of Assessment for UE in Q3

  • Presentation and/or works
  • Written examination

Q3 UE Assessment Comments

same as Q1

Type of Resit Assessment for UE in Q1 (BAB1)

  • N/A

Q1 UE Resit Assessment Comments (BAB1)

n/a

Type of Teaching Activity/Activities

AAType of Teaching Activity/Activities
I-MARO-202
  • Cours magistraux
  • Travaux pratiques
I-ILIA-202
  • Cours magistraux
  • Travaux pratiques
  • Projet sur ordinateur
  • Etudes de cas

Mode of delivery

AAMode of delivery
I-MARO-202
  • Mixed
I-ILIA-202
  • Mixed

Required Reading

AA
I-MARO-202
I-ILIA-202

Required Learning Resources/Tools

AARequired Learning Resources/Tools
I-MARO-202Not applicable
I-ILIA-202Not applicable

Recommended Reading

AA
I-MARO-202
I-ILIA-202

Recommended Learning Resources/Tools

AARecommended Learning Resources/Tools
I-MARO-202Not applicable
I-ILIA-202Not applicable

Other Recommended Reading

AAOther Recommended Reading
I-MARO-202Not applicable
I-ILIA-202Not applicable

Grade Deferrals of AAs from one year to the next

AAGrade Deferrals of AAs from one year to the next
I-MARO-202Unauthorized
I-ILIA-202Unauthorized
(*) 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