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-2022

    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
    Assistants
    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.

    Condition of access

    Please register to the course on the students portal < https://my.unifr.ch >; in case of problems write an email with your name, Nr SIUS, Code and Course Name to Stephanie Fasel < stephanie.fasel@unifr.ch >.
    All official communication will go through Moodle, please register at your earliest convenience < https://moodle.unifr.ch/course/view.php?id=256367 >. All lectures will be online for the time being, using Microsoft Teams: access details will be made available on Moodle.

    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
    21.02.2022 14:15 - 17:00 Cours PER 21, Room E140
    28.02.2022 14:15 - 17:00 Cours PER 21, Room E140
    07.03.2022 14:15 - 17:00 Cours PER 21, Room E140
    14.03.2022 14:15 - 17:00 Cours PER 21, Room E140
    21.03.2022 14:15 - 17:00 Cours PER 21, Room E140
    28.03.2022 14:15 - 17:00 Cours PER 21, Room E140
    04.04.2022 14:15 - 17:00 Cours PER 21, Room E140
    11.04.2022 14:15 - 17:00 Cours PER 21, Room E140
    25.04.2022 14:15 - 17:00 Cours PER 21, Room E140
    02.05.2022 14:15 - 17:00 Cours PER 21, Room E140
    09.05.2022 14:15 - 17:00 Cours PER 21, Room E140
    16.05.2022 14:15 - 17:00 Cours PER 21, Room E140
    23.05.2022 14:15 - 17:00 Cours PER 21, Room E140
    30.05.2022 14:15 - 17:00 Cours PER 21, Room E140
  • Assessments methods

    Written exam - SP-2022, Session d'été 2022

    Date 23.06.2022 14:00 - 16:00
    Assessments methods By rating
    Descriptions of Exams

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

    Written exam - SP-2022, Autumn Session 2022

    Assessments methods By rating
    Descriptions of Exams

    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 > 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)