Pattern recognition

  • Teaching

    Details

    Faculty Faculty of Science and Medicine
    Domain Computer Science
    Code IN.8608
    Languages English
    Type of lesson Lecture
    Level Master
    Semester SP-2020

    Schedules and rooms

    Summary schedule Monday 14:15 - 17:00, Hebdomadaire, PER 21, Room D230
    Struct. of the schedule 3h par semaine durant 14 semaines
    Contact's hours 42

    Teaching

    Responsibles
    Teachers
    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.
    Training objectives 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.

    Comments

    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
    No
    Off field
    No
    BeNeFri
    Yes
    Mobility
    Yes
    UniPop
    No
  • Dates and rooms
    Date Hour Type of lesson Place
    17.02.2020 14:15 - 17:00 Cours PER 21, Room D230
    24.02.2020 14:15 - 17:00 Cours PER 21, Room D230
    02.03.2020 14:15 - 17:00 Cours PER 21, Room D230
    09.03.2020 14:15 - 17:00 Cours PER 21, Room D230
    16.03.2020 14:15 - 17:00 Cours PER 21, Room D230
    23.03.2020 14:15 - 17:00 Cours PER 21, Room D230
    30.03.2020 14:15 - 17:00 Cours PER 21, Room D230
    06.04.2020 14:15 - 17:00 Cours PER 21, Room D230
    20.04.2020 14:15 - 17:00 Cours PER 21, Room D230
    27.04.2020 14:15 - 17:00 Cours PER 21, Room D230
    04.05.2020 14:15 - 17:00 Cours PER 21, Room D230
    11.05.2020 14:15 - 17:00 Cours PER 21, Room D230
    18.05.2020 14:15 - 17:00 Cours PER 21, Room D230
    25.05.2020 14:15 - 17:00 Cours PER 21, Room D230
  • Assessments methods

    Examen écrit

    Assessments methods By rating
  • Assignment
    Valid for the following curricula:
    Additional Courses in Sciences
    Version: ens_compl_sciences
    Paquet indépendant des branches > Specialized courses in Computer Science (Master level)

    Branche secondaire Master: informatique de gestion
    Version: 2019/SA-v01
    Cours > Module Informatik > Advanced Information Processing
    Cours > Module Informatik > Data Science
    Cours > Module Wirtschaftsinformatik > DADS: Data Analytics & Decision Support

    Business Communication - Information Systems 90 ECTS [MA]
    Version: 2019/SA-v01
    Courses - 60 ECTS > Option Group > Information Management > Cours > Module Informatik > Data Science
    Courses - 60 ECTS > Option Group > Information Management > Cours > Module Informatik > Advanced Information Processing
    Courses - 60 ECTS > Option Group > Information Management > Cours > Module Wirtschaftsinformatik > DADS: Data Analytics & Decision Support

    Information Management 90 ECTS [MA]
    Version: 2015
    Cours > Module Informatik > Advanced Information Processing
    Cours > Module Informatik > Data Science

    Information Management 90 ECTS [MA]
    Version: 2017/SA_v01
    Cours > Module Informatik > Data Science
    Cours > Module Informatik > Advanced Information Processing

    Information Management 90 ECTS [MA] - SA/2019
    Version: 2019/SA_V01
    Classes - min. 45 ECTS > Module IT and IT Management > Data Science
    Classes - min. 45 ECTS > Module IT and IT Management > Advanced Information Processing
    Classes - min. 45 ECTS > Module IT and IT Management > DADS: Data Analytics & Decision Support
    Classes - min. 45 ECTS > Modules IT Management - min. 22 ECTS > DADS: Data Analytics & Decision Support

    MSc in Computer science (BeNeFri)
    Version: 2010_2/V_02
    MSc in Computer science (BeNeFri), lectures, seminars and Master thesis > T3: Advanced Information Processing
    MSc in Computer science (BeNeFri), lectures, seminars and Master thesis > T6: Data Science
    MSc in Computer science (BeNeFri), lectures, seminars and Master thesis > Specialized courses in Computer Science (Master level)