Pattern recognition

  • Enseignement

    Détails

    Faculté Faculté des sciences et de médecine
    Domaine Informatique
    Code IN.8608
    Langues Anglais
    Type d'enseignement Cours
    Cursus Master
    Semestre(s) SP-2020

    Horaires et salles

    Horaire résumé Lundi 14:15 - 17:00, Hebdomadaire, PER 21, salle D230
    Struct. des horaires 3h par semaine durant 14 semaines
    Heures de contact 42

    Enseignement

    Responsables
    Enseignants
    Description In this course, we study the fundaments of pattern recognition. We adopt an engineering point of view on the development of intelligent machines which are able to identify patterns in data. The core methods and algorithms are elaborated that enable pattern recognition for a wide range of data sources including sensory data (image, video, audio, location, etc.) as well as born-digital data (text, network traffic, chemical formulas, etc.). The course is organized in two parts. In the first part, we explore statistical pattern recognition based on feature vector representation. Standard methods for unsupervised clustering and supervised classification in vector spaces will be discussed. In the second part, we investigate structural pattern recognition based on string and graph representation. For clustering and classification of structural data, dissimilarity measures will be introduced alongside with explicit and implicit vector space embedding approaches. The course is accompanied by practical exercises that involve the implementation of algorithms discussed in class and their application to exemplary pattern recognition tasks.
    Objectifs de formation On successful completion of this class, you will be able to:

    - Design pattern recognition systems for a large variety of data sources, namely to cluster and classify objects represented as feature vectors, feature vector sequences, strings, and graphs.

    - Describe the mathematical techniques, assumptions, and relevant parameters of the underlying recognition algorithms, including k-means clustering, Bayes classification, support vector machines, neural networks, hidden Markov models, graph edit distance, and graph kernel functions.

    - Apply the pattern recognition systems to exemplary recognition tasks ranging from image recognition over movement analysis to the classification of molecular compounds.

    Commentaire

    MSc-CS BENEFRI - (Code Ue: 33082 / Track: T3; Code Ue: 63082 / Track: T6) The exact date and time of this course as well as the complete course list can be found at http://mcs.unibnf.ch/.

     

    Softskills
    Non
    Hors domaine
    Non
    BeNeFri
    Oui
    Mobilité
    Oui
    UniPop
    Non
  • Dates et salles
    Date Heure Type d'enseignement Lieu
    17.02.2020 14:15 - 17:00 Cours PER 21, salle D230
    24.02.2020 14:15 - 17:00 Cours PER 21, salle D230
    02.03.2020 14:15 - 17:00 Cours PER 21, salle D230
    09.03.2020 14:15 - 17:00 Cours PER 21, salle D230
    16.03.2020 14:15 - 17:00 Cours PER 21, salle D230
    23.03.2020 14:15 - 17:00 Cours PER 21, salle D230
    30.03.2020 14:15 - 17:00 Cours PER 21, salle D230
    06.04.2020 14:15 - 17:00 Cours PER 21, salle D230
    20.04.2020 14:15 - 17:00 Cours PER 21, salle D230
    27.04.2020 14:15 - 17:00 Cours PER 21, salle D230
    04.05.2020 14:15 - 17:00 Cours PER 21, salle D230
    11.05.2020 14:15 - 17:00 Cours PER 21, salle D230
    18.05.2020 14:15 - 17:00 Cours PER 21, salle D230
    25.05.2020 14:15 - 17:00 Cours PER 21, salle D230
  • Modalités d'évaluation

    Examen écrit

    Mode d'évaluation Par note
  • Affiliation
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