Machine learning
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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-2022 Title
French Apprentissage automatique German Maschinelles Lernen English Machine learning Schedules and rooms
Summary schedule Monday 14:15 - 17:00, Hebdomadaire (Spring semester)
Struct. of the schedule 2h +2h par semaine durant 14 semaines Contact's hours 56 Teaching
Responsibles - Cudré-Mauroux Philippe
Teachers - Cuccu Giuseppe
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 Only for Master Students of the SES Faculty with finished Bachelor / Pre-Master Students SES Faculty not allowed!
Voir Règlement du 6 avril 2020 pour l’obtention des Bachelor of Science et des Master of Science, Art. 7.
Comments Cours Master SES /
Students of the SES faculty: registration only possible with acomplished BachelorRegistration to the cours AND exams is mandatory and does not automatically happen if you are registered to a class. Please observe the deadlines of the faculty of science and medicine.
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 Written exam - SP-2022, Autumn Session 2022
Date 08.09.2022 14:00 - 16:00 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)
Additional programme requirements for PhD studies [PRE-DOC]
Version: 2020_1/v_01
Additional programme requirements for PhD studies (Faculty of Science and Medicine) > Specialized courses in Computer Science (Master level)
Computer Science [3e cycle]
Version: 2015_1/V_01
Continuing education > Specialized courses in Computer Science (Master level)
Computer Science [POST-DOC]
Version: 2015_1/V_01
Continuing education > Specialized courses in Computer Science (Master level)
Digital Neuroscience (Specialised Master) 120 [MA]
Version: 2023_1/V_01
sp-MSc in Digital Neuroscience, compulsory courses (practical courses, projects, seminars) > sp-MSc in Digital Neuroscience, compulsory courses (from AS2023 on)
Ma - Accounting and Finance - 120 ECTS
Version: 2024/SP_V01_DD_Caen
UniFr courses > Modules "Data Analytics" and "Audit et Fiscalité": min. 2 courses > DAT: Data Analytics
Ma - Accounting and Finance - 90 ECTS
Version: 2021/SA_V01 Dès SA-2024
Course - 72 ECTS > Modules "Data Analytics" and "Audit et Fiscalité": min. 3 courses > DAT: Data Analytics > Core courses
Ma - Business Communication : Business Informatics - 90 ECTS
Version: 2020/SA_V02
Courses - 60 ECTS > Option Group > Information Management > Cours > Modules management > 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 - Data Analytics & Economics - 90 ECTS
Version: 2020/SA-v01
Courses min 63 ECTS > Mandatory Modules (45 to 63 ECTS) > Module I: Data Analytics (Data)
Ma - Economics - 90 ECTS
Version: 2021/SA_V04
Le choix de l'option se fait par l'inscription au premier cours dans l'une des options possibles. > Quantitative Economics
Ma - International and European Business - 90 ECTS
Version: 2021/SA_v01 dès SA-2024
Courses > Modules > One complete module taken from the following list > DAT Module validation element group > DAT: Data Analytics > Core coursesCourses > Modules > Elective courses of the management modules > Elective courses of the management modules > Elective courses for the Master in management
Ma - Management - 90 ECTS
Version: 2021/SA_v03 dès SA-2024
Courses: min. 72 ECTS > Modules - min 54 ECTS > Minimum of 3 modules with a minimum of 18 ECTS and 2 core courses > DAT Module validation element group > DAT: Data Analytics > Core coursesCourses: min. 72 ECTS > Modules - min 54 ECTS > Elective courses taken outside a validating module > Elective courses in the management modules > Elective courses for the Master in management
Ma - Marketing - 90 ECTS
Version: 2021/V03 dès SA-2024
Courses - 72 ECTS > Complementary module > DAT Module validation element group > DAT: Data Analytics > Core courses
MiMa - Business Informatics - 30 ECTS
Version: 2020/SA_V01
Cours > Modules management > DAT: Data Analytics
MiMa - Data Analytics - 30 ECTS
Version: 2020/SA-v01
À choix 18 crédits ECTS
MiMa - Gestion d'entreprise - 30 ECTS
Version: 2021/SA_V01
Elective courses - 30 ECTS > DAT: Data Analytics