Study programme 2021-2022Français
Machine Learning I
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-120-MCompulsory UEBEN TAIEB SouhaibS861 - Big Data and Machine Learning
  • BEN TAIEB Souhaib

Language
of instruction
Language
of assessment
HT(*) HTPE(*) HTPS(*) HR(*) HD(*) CreditsWeighting Term
  • Anglais, Français
Anglais, Français303000066.002nd term

AA CodeTeaching Activity (AA) HT(*) HTPE(*) HTPS(*) HR(*) HD(*) Term Weighting
S-INFO-256Machine Learning I3030000Q2100.00%
Programme component

Objectives of Programme's Learning Outcomes

  • Manage research, development and innovation.
    • Understand unprecedented problems in computer science and its applications.
  • 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.
    • Demonstrate thoroughness, independence, creativity, intellectual honesty, and ethical values.

Learning Outcomes of UE

The course introduces statistical and machine learning methods for predictive modelling based on big datasets. The unit covers, among other topics, linear and non-linear methods for regression, classification, clustering and dimensionality reduction. 

Content of UE

See the single learning activity.
 

Prior Experience

Basics of Probability and Statistics
Basics of Matrix Algebra
Basics of Non-linear optimization

Type of Assessment for UE in Q2

  • Presentation and/or works
  • Written examination
  • Graded tests

Q2 UE Assessment Comments

Written exam (60% of total score)
Project (20% of total score)
Assignments (20% of total score)
There is a hurdle of 50% for each of the previous evaluations

Type of Assessment for UE in Q3

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

Q3 UE Assessment Comments

Oral exam (60% of total score)
Project (20% of total score)
Assignments (20% of total score)
There is a hurdle of 50% for each of the previous evaluations

Type of Teaching Activity/Activities

AAType of Teaching Activity/Activities
S-INFO-256
  • Cours magistraux
  • Travaux pratiques

Mode of delivery

AAMode of delivery
S-INFO-256
  • Mixed

Required Reading

AA
S-INFO-256

Required Learning Resources/Tools

AARequired Learning Resources/Tools
S-INFO-256Not applicable

Recommended Reading

AA
S-INFO-256

Recommended Learning Resources/Tools

AARecommended Learning Resources/Tools
S-INFO-256Not applicable

Other Recommended Reading

AAOther Recommended Reading
S-INFO-256Not applicable

Grade Deferrals of AAs from one year to the next

AAGrade Deferrals of AAs from one year to the next
S-INFO-256Unauthorized
(*) 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