Statistical learning and neural networks

  • Teaching

    Details

    Faculty Faculty of Science and Medicine
    Domain Mathematics
    Code UE-SMA.03413
    Languages French , English
    Type of lesson Lecture
    Level Bachelor
    Semester SA-2019

    Schedules and rooms

    Summary schedule Monday 13:15 - 15:00, Hebdomadaire (Autumn semester)
    Hours per week
    		2

    Teaching

    Responsibles
    • Mazza Christian
    Teachers
    • Mazza Christian
    Description

    This course aims at presenting and studying mathematically models from statistical learning theory, by focusing on deep learning algorithms in multi-layer neural networks. Applications to artificial intelligence will be provided. We will also present and study the basic probabilistic and statistical models of machine learning by making links with neural nets. A special emphasis will be given to deep learning processes for Boltzmann machines using methods from probability theory, statistics and statistical mechanics. Practical examples from arti cial intelligence will be considered using both theoretical and programming approaches.
    Training objectives

    Topics focus on but are not limited to:
    Biological neural networks, mathematical models of neural nets, learning algorithms (Hebb rule) for Hop eld neural nets and Boltzmann machines, machine learning and multivariate statistics (discriminant analysis, regression, support vector machines), general statistical learning theory (deterministic and stochastic models, Vapnik-Cervonenkis theory), learning processes
    in associative memories, perceptron and multi-layer neural nets, stochastic processes for deep learning in Boltzmann machines, thermodynamic extension (statistical mechanics of learning processes), self-organisation, chemical reaction networks and machine learning. If time permits we will also consider recent methods from topology and geometry like manifold learning and
    topological data analysis.
    Softskills No
    Off field No
    BeNeFri Yes
    Mobility Yes
    UniPop No
  • Dates and rooms
    Date Hour Type of lesson Place
    16.09.2019 13:15 - 15:00 Cours PER 14, Room 0.026
    23.09.2019 13:15 - 15:00 Cours PER 14, Room 0.026
    30.09.2019 13:15 - 15:00 Cours PER 14, Room 0.026
    07.10.2019 13:15 - 15:00 Cours PER 14, Room 0.026
    14.10.2019 13:15 - 15:00 Cours PER 14, Room 0.026
    21.10.2019 13:15 - 15:00 Cours PER 14, Room 0.026
    28.10.2019 13:15 - 15:00 Cours PER 14, Room 0.026
    04.11.2019 13:15 - 15:00 Cours PER 14, Room 0.026
    11.11.2019 13:15 - 15:00 Cours PER 14, Room 0.026
    18.11.2019 13:15 - 15:00 Cours PER 14, Room 0.026
    25.11.2019 13:15 - 15:00 Cours PER 14, Room 0.026
    02.12.2019 13:15 - 15:00 Cours PER 14, Room 0.026
    09.12.2019 13:15 - 15:00 Cours PER 14, Room 0.026
    16.12.2019 13:15 - 15:00 Cours PER 14, Room 0.026
  • Assessments methods

    Oral exam - SA-2019, Session de printemps 2020

    Assessments methods By rating
    Descriptions of Exams

    COVID-19 - SS2020 / Exam session SUMMER 2020

    Oral Exam with physical presence

    Duration: 20' or 30' minutes

    Oral exam - SP-2020, Session d'été 2020

    Assessments methods By rating
    Descriptions of Exams

    COVID-19 - SS2020 / Exam session SUMMER 2020

    Oral Exam with physical presence

    Duration: 20' or 30' minutes

    Oral exam - SP-2020, Autumn Session 2020

    Assessments methods By rating
    Descriptions of Exams

    COVID-19 - SS2020 / Exam session SUMMER 2020

    Oral Exam with physical presence

    Duration: 20' or 30' minutes

    Oral exam - SP-2021, Session d'été 2021

    Assessments methods By rating
    Descriptions of Exams

    COVID-19 - SS2020 / Exam session SUMMER 2020

    Oral Exam with physical presence

    Duration: 20' or 30' minutes
  • Assignment
    Valid for the following curricula:
    Additional Courses in Sciences
    Version: ens_compl_sciences
    Paquet indépendant des branches > Advanced courses in Mathematics (Bachelor level)
    Additional Programme Requirements to the MSc in Computer Science [MA]
    Version: 2022_1/V_01
    Supplement to the MSc in Computer science > Advanced courses in Mathematics (Bachelor level)
    Additional Programme Requirements to the MSc in Mathematics [MA]
    Version: 2022_1/V_01
    Supplement to the MSc in Mathematics > Advanced courses in Mathematics (Bachelor level)
    Additional TDHSE programme in Mathematics
    Version: 2022_1/V_01
    Additional TDHSE Programme Requirements for Mathematics 60 or +30 > Programme 60 or +30 > Additional Programme Requirements to Mathematics 60 > Additional TDHSE programme for Mathematics 60 (from AS2018 on)
    Mathematics 120
    Version: 2022_1/V_01
    BSc in Mathematics, Major, 2nd-3rd year > Mathematics, Major, 2nd and 3rd years, elective courses (from AS2018 on)
    Mathematics +30 [MA] 30
    Version: 2022_1/V_01
    Minor in Mathematics +30 (MATH+30 for 90 ECTS) > Mathematics +30, Module C (from AS2020 on)
    Mathematics [3e cycle]
    Version: 2015_1/V_01
    Continuing education > Advanced courses in Mathematics (Bachelor level)
    Mathematics [POST-DOC]
    Version: 2015_1/V_01
    Continuing education > Advanced courses in Mathematics (Bachelor level)
    Pre-Master-Programme to the MSc in Mathematics [PRE-MA]
    Version: 2022_1/V_01
    Prerequisite to the MSc in Mathematics > Advanced courses in Mathematics (Bachelor level)