Machine learning
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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-2021 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
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.
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 22.02.2021 14:15 - 17:00 Cours PER 21, Room C230 01.03.2021 14:15 - 17:00 Cours PER 21, Room C230 08.03.2021 14:15 - 17:00 Cours PER 21, Room C230 15.03.2021 14:15 - 17:00 Cours PER 21, Room C230 22.03.2021 14:15 - 17:00 Cours PER 21, Room C230 29.03.2021 14:15 - 17:00 Cours PER 21, Room C230 12.04.2021 14:15 - 17:00 Cours PER 21, Room C230 19.04.2021 14:15 - 17:00 Cours PER 21, Room C230 26.04.2021 14:15 - 17:00 Cours PER 21, Room C230 03.05.2021 14:15 - 17:00 Cours PER 21, Room C230 10.05.2021 14:15 - 17:00 Cours PER 21, Room C230 17.05.2021 14:15 - 17:00 Cours PER 21, Room C230 31.05.2021 14:15 - 17:00 Cours PER 21, Room C230 -
Assessments methods
Written exam - SP-2021, Session d'été 2021
Date 24.06.2021 14:00 - 16:00 Assessments methods By rating Descriptions of Exams Selon modalité A de l'annexe du plan d'études en informatique
Comment Written exam in presence / 120 minutes / open book
Written exam - SP-2021, Autumn Session 2021
Date 08.09.2021 14:00 - 16:00 Assessments methods By rating Descriptions of Exams Selon modalité A de l'annexe du plan d'études en informatique
Comment Written exam in presence / 120 minutes / open book
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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)