Machine learning

  • Teaching

    Details

    Faculty Faculty of Science and Medicine
    Domain Computer Science
    Code UE-SIN.08022
    Languages English
    Type of lesson Lecture
    Level Master
    Semester SP-2020

    Title

    French Apprentissage automatique
    German Maschinelles Lernen
    English Machine learning

    Schedules and rooms

    Summary schedule Monday 14:15 - 17:00, Hebdomadaire, PER 21, Room C230
    Struct. of the schedule 2h +2h par semaine durant 14 semaines
    Contact's hours 56

    Teaching

    Responsibles
    Teachers
    Description

    The goal of this course is to understand the foundation of Machine Learning as a field, providing the basis to master its many branches and applications. After an introduction about how machines learn, the focus will be on a short selection of key algorithms for supervised, unsupervised and reinforcement learning. The students will learn how parametrized function approximators can be used to take decisions, how to update their parametrization to modify their behavior, and how to leverage data and interactions in real-world applications.

    The course is composed of theoretical lectures, explaining the inner working and intuitions behind the methods, interleaved with practical sessions to gain hands-on experience. The students will be introduced to both re-implementing (and customizing) some of the basic algorithms, and to applying the current standard libraries on practical applications.
    The theoretical requirements include a basic understanding of linear algebra, calculus and statistic, as far as appropriate to understand the algorithms' inner workings. The practical sessions require a degree of familiarity with the Python programming language, and a working installation for the exercises. All lectures and material will be in English.

    Training objectives

    This course is set to provide a fundamental understanding of what is Machine Learning and how all of its methods operate, with practical experience on selected algorithms and libraries. The core objective is to support and facilitate further learning on the topic, both in the form of further self-study and as an introduction for advanced classes available in the Masters course.

    Comments

    Cours pour Master SES

    Les unités d’enseignement se composent généralement de deux heures de cours et deux heures d’exercices par semaine. Nous vous prions de bien vouloir vous conformer aux délais d’inscriptions aux épreuves de la Faculté des sciences et de médecine.

    Softskills
    No
    Off field
    No
    BeNeFri
    Yes
    Mobility
    Yes
    UniPop
    No

    Documents

    Bibliography

    - Y. Abu-Mostafa, M. Magdon-Ismail, L. Hsuan-Tien. Learning from Data. AMLBook.
      http://amlbook.com/
    - M. Bishop. Pattern Recognition and Machine Learning. Springer.
      https://www.springer.com/gp/book/9780387310732
    - I. Goodfellow, Y. Bengio, A. Courville. Deep Learning. MIT Press.
      https://www.deeplearningbook.org/
    - R. Sutton, A. Barto. Reinforcement Learning. MIT Press.
    https://mitpress.mit.edu/books/reinforcement-learning-second-edition

  • Dates and rooms
    Date Hour Type of lesson Place
    17.02.2020 14:15 - 17:00 Cours PER 21, Room C230
    24.02.2020 14:15 - 17:00 Cours PER 21, Room C230
    02.03.2020 14:15 - 17:00 Cours PER 21, Room C230
    09.03.2020 14:15 - 17:00 Cours PER 21, Room C230
    16.03.2020 14:15 - 17:00 Cours PER 21, Room C230
    23.03.2020 14:15 - 17:00 Cours PER 21, Room C230
    30.03.2020 14:15 - 17:00 Cours PER 21, Room C230
    06.04.2020 14:15 - 17:00 Cours PER 21, Room C230
    20.04.2020 14:15 - 17:00 Cours PER 21, Room C230
    27.04.2020 14:15 - 17:00 Cours PER 21, Room C230
    04.05.2020 14:15 - 17:00 Cours PER 21, Room C230
    11.05.2020 14:15 - 17:00 Cours PER 21, Room C230
    18.05.2020 14:15 - 17:00 Cours PER 21, Room C230
    25.05.2020 14:15 - 17:00 Cours PER 21, Room C230
  • Assessments methods

    Examen écrit - SP-2020, Session d'été 2020

    Assessments methods By rating
    Descriptions of Exams

    COVID-19 - SS2020 / Exam session SUMMER 2020

    Written Online-Exam

    Duration: 120 minutes

     

     

    Selon modalité A de l'annexe du plan d'études en informatique

    Examen écrit - SP-2020, Autumn Session 2020

    Assessments methods By rating
    Descriptions of Exams

    COVID-19 - SS2020 / Exam session SUMMER 2020

    Written Online-Exam

    Duration: 120 minutes

     

     

    Selon modalité A de l'annexe du plan d'études en informatique

  • 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)

    Data Analytics & Economics 90 [MA]
    Version: 2020/SA-v01
    Courses min 63 ECTS > Mandatory Modules 45 ECTS > Module I: Data Analytics (Data)

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