Study programme 2020-2021Français
Computer Vision and Machine Intelligence
Programme component of Master's in Computer Science à la Faculty of Science

Students are asked to consult the ECTS course descriptions for each learning activity (AA) to know what special Covid-19 assessment methods are possibly planned for the end of Q3

CodeTypeHead of UE Department’s
contact details
US-M1-INFO60-045-MOptional UEGOSSELIN BernardF105 - Information, Signal et Intelligence artificielle
  • GOSSELIN Bernard
  • MANCAS Matei

of instruction
of assessment
HT(*) HTPE(*) HTPS(*) HR(*) HD(*) CreditsWeighting Term
  • Anglais
Anglais242400044.001st term

AA CodeTeaching Activity (AA) HT(*) HTPE(*) HTPS(*) HR(*) HD(*) Term Weighting
I-ISIA-005Computer Vision & Machine Intelligence 2424000Q1100.00%
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.
  • Carry out development or innovation projects in IT.
    • Apply, mobilise, articulate and promote the knowledge and skills acquired in order to contribute to the achievement of a development or innovation project.
    • Master the complexity of such work and take into account the objectives and constraints which characterise it.
  • Master communication techniques.
    • Communicate, both orally and in writing, their findings, original proposals, knowledge and underlying principles, in a clear, structured and justified manner.
    • Where possible, communicate in a foreign language.
  • Apply scientific methodology.
    • Critically reflect on the impact of IT in general, and on the contribution to projects.

Learning Outcomes of UE

develop an applied pattern recognition system, together with a critical analysis of the problem;
apply image analysis and segmentation techniques
apply data processing techniques (feature extraction, feature selection);
apply classification and machine learning techniques (Gaussian models, Clustering, Artificial Neural Networks, Dynamic Time Warping, Hidden Markov Models, Deep Neural Networks);
estimate performances of classifiers.

Content of UE

Image Processing: Image acquisition; lowlevel processing, filtering, transforms; image segmentation and registration;
Pattern Recognition: SPR scheme, feature extraction, classifiers, combining classifiers; neural networks:feed-forward neural networks, training MLP, Deep Neural Nets; support vector machines; dynamic systems: dynamic time warping, hidden Markov models

Prior Experience

fundamentals of signal processing; probability and statistics

Type of Assessment for UE in Q1

  • Oral examination

Q1 UE Assessment Comments

Not applicable

Type of Assessment for UE in Q3

  • Oral examination

Q3 UE Assessment Comments

Not applicable

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
  • Cours magistraux
  • Travaux pratiques
  • Projet sur ordinateur
  • Etudes de cas

Mode of delivery

AAMode of delivery
  • Mixed

Required Reading


Required Learning Resources/Tools

AARequired Learning Resources/Tools
I-ISIA-005Not applicable

Recommended Reading


Recommended Learning Resources/Tools

AARecommended Learning Resources/Tools
I-ISIA-005Not applicable

Other Recommended Reading

AAOther Recommended Reading
I-ISIA-005Sergios Theodoridis, Aggelos Pikrakis, Konstantinos Koutroumbas, Dionisis Cavouras, "Introduction to pattern recognition - A MATLAB approach", 9780123744869
T. Dutoit & F. Marques, "Applied Signal Processing", Springer, 2009
R.O. Duda & P.E. Hart, "Pattern Classification and Scene Analysis", John Wiley & Sons, 1973 (2000).
K. Fukunaga, "Introduction to Statistical Pattern Recognition", Academic Press, San Diego, 1990

Grade Deferrals of AAs from one year to the next

AAGrade Deferrals of AAs from one year to the next
(*) 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 : 09/07/2021
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