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MACHINE LEARNING

CODE 90498
ACADEMIC YEAR 2022/2023
CREDITS
  • 5 cfu during the 2nd year of 10720 ENVIRONMENTAL ENGINEERING (LM-35) - GENOVA
  • 5 cfu during the 2nd year of 10376 INGEGNERIA CHIMICA E DI PROCESSO (LM-22) - GENOVA
  • 9 cfu during the 1st year of 10852 COMPUTER SCIENCE (LM-18) - GENOVA
  • SCIENTIFIC DISCIPLINARY SECTOR INF/01
    LANGUAGE English
    TEACHING LOCATION
  • GENOVA
  • SEMESTER 1° Semester
    TEACHING MATERIALS AULAWEB

    OVERVIEW

    The goal of this course is to provide an overview of classical Machine Learning algorithms, discussing modeling and computational aspects.

    AIMS AND CONTENT

    LEARNING OUTCOMES

    Learning how to use classical supervised and unsupervised machine learning algorithms by grasping the underlying computational and modeling issues.

    AIMS AND LEARNING OUTCOMES

    Students will be provided with basic ideas behind statistical learning and a number of prototypical supervised approaches, including, local methods, regularization networks, linear and non linear models. The Course also covers basic unsupervised problems such as clustering and dimensionality reduction. Special effort is devoted to discussing how to set up a reliable machine learning pipeline.

    Students will be involved in project activities.

    PREREQUISITES

    Basic probability, calculus, linear algebra, programming.

    TEACHING METHODS

    Classes and practical lab sessions. 

    SYLLABUS/CONTENT

    Course content

    • Machine Learning basics
    • Empirical risk minimization
    • Feature maps and kernels
    • Variable selection and dimensionality reduction
    • Clustering
    • Neural Networks

    RECOMMENDED READING/BIBLIOGRAPHY

    Material provided by the instructors (slides and papers), see the course Aulaweb page additional references.

    TEACHERS AND EXAM BOARD

    Exam Board

    NICOLETTA NOCETI (President)

    ELENA NICORA

    LORENZO ROSASCO (President Substitute)

    ALESSANDRO VERRI (Substitute)

    LESSONS

    Class schedule

    All class schedules are posted on the EasyAcademy portal.

    EXAMS

    EXAM DESCRIPTION

    • 40% continuous assessment
    • 20% project (in groups)
    • 40% theory oral  

    ASSESSMENT METHODS

    • timely delivery of assignments
    • active participation in class
    • final project on a method or use-case, and presentation of the obtained results in a seminar
    • oral exam

    Exam schedule

    Date Time Location Type Notes
    20/12/2022 09:00 GENOVA Esame su appuntamento
    19/01/2023 09:30 GENOVA Orale
    19/01/2023 09:30 GENOVA Scritto
    31/01/2023 09:00 GENOVA Esame su appuntamento
    02/02/2023 09:30 GENOVA Orale
    02/02/2023 09:30 GENOVA Scritto
    30/05/2023 09:00 GENOVA Esame su appuntamento
    15/06/2023 09:30 GENOVA Orale
    15/06/2023 09:30 GENOVA Scritto
    04/07/2023 09:00 GENOVA Esame su appuntamento
    06/07/2023 09:30 GENOVA Scritto
    06/07/2023 09:30 GENOVA Orale
    29/08/2023 09:00 GENOVA Esame su appuntamento
    11/09/2023 09:30 GENOVA Scritto