Machine learning

  • Teaching

    Details

    Faculty Faculty of Science and Medicine
    Domain Computer Science
    Code UE-SIN.06022
    Languages English
    Type of lesson Lecture
    Level Bachelor
    Semester SP-2020

    Title

    French Apprentissage automatique
    German Maschinelles Lernen
    English Machine learning

    Schedules and rooms

    Summary schedule Monday 14:15 - 17:00, Hebdomadaire
    Struct. of the schedule 2+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

    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

    Written exam - 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

    Requirements L'admission à l'examen est soumise à la condition d'avoir passé 10/13 des devoirs au 31.05.2020, selon les critères de validation de l'examen définis dans les informations du cours.

    Written exam - SP-2020, Autumn Session 2020

    Date 10.09.2020 14:00 - 16:00
    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

    Comment examen oral en présence
    20 min de préparation et 20 min d'examen
  • Assignment
    Valid for the following curricula:
    Additional Courses in Sciences
    Version: ens_compl_sciences
    Paquet indépendant des branches > Advanced courses in Computer Science (Bachelor level)

    Additional Programme Requirements to the MSc in Bioinformatics and Computational Biology [MA]
    Version: 2020_1/V_01
    Additional Programme Requirements to the MSc Bioinformatics and Computational Biology > Advanced courses in Computer Science (Bachelor level)

    Ba - Business Informatics - 180 ECTS
    Version: 2020/SA_V02
    3nd year 60 ECTS > 3rd year courses > Cours obligatoires / Pflichtkurse 32.5 ECTS > Apprentissage automatique / Maschinelles Lernen / Machine learning

    Ba - Economics - 180 ECTS
    Version: 2018/SA_V02
    3nd year 60 ECTS > 3rd year courses > Wahlkurse in der Wirtschaftsinformatik für Volkswirtschaftslehre 180 ECTS - HS 2018 - 3. Jahr > Apprentissage automatique / Maschinelles Lernen / Machine learning

    Computer Science [3e cycle]
    Version: 2015_1/V_01
    Continuing education > Advanced courses in Computer Science (Bachelor level)

    Computer Science [POST-DOC]
    Version: 2015_1/V_01
    Continuing education > Advanced courses in Computer Science (Bachelor level)

    Ma - Accounting and Finance - 90 ECTS
    Version: 2021/SA_V01
    Course - 72 ECTS > Modules "Data Analytics" and "Audit et Fiscalité": min. 3 courses > DAT: Data Analytics

    Ma - Business Communication : Business Informatics - 90 ECTS
    Version: 2020/SA_V01
    Courses - 60 ECTS > Option Group > Information Management > Cours > Modules management > DAT: Data Analytics

    Ma - Business Communication : Management - 90 ECTS
    Version: 2021/SA_V01
    Courses - 60 ECTS > Chosen Option > Management > 30 ECTS parmi les modules : > DAT: Data Analytics

    Ma - Business Informatics - 90 ECTS
    Version: 2020/SA-v01
    Classes - min. 45 ECTS > Modules management - max. 15 ECTS > DAT: Data Analytics

    Ma - International and European Business - 90 ECTS
    Version: 2021/SA_V01
    Courses > Modules > One complete module taken from the following list > Groupe d'élément de validation du Module DAT > DAT: Data Analytics

    Ma - Management - 90 ECTS
    Version: 2021/SA_V01
    Courses: min. 72 ECTS > Modules - min 54 ECTS > Groupe d'élément de validation du Module DAT > DAT: Data Analytics

    Ma - Marketing - 90 ECTS
    Version: 2021/SA_V02
    Courses > Groupe d'élément de validation du Module DAT > DAT: Data Analytics

    MiBa - Computer Management - 60 ECTS
    Version: 2021/SA_V03
    Register in the option corresponding to your situation. > Standard > Min. 18 ECTS from the list > Machine Learning

    MiMa - Business Informatics - 30 ECTS
    Version: 2020/SA_V01
    Cours > Modules management > DAT: Data Analytics

    MiMa - Gestion d'entreprise - 30 ECTS
    Version: 2021/SA_V01
    Elective courses - 30 ECTS > DAT: Data Analytics

    Pre-Master-Programme to the MSc in Bioinformatics and Computational Biology [PRE-MA]
    Version: 2020_1/V_01
    Prerequisite to the MSc in Bioinformtics and Computational Biology > Advanced courses in Computer Science (Bachelor level)