Salta al contenuto principale della pagina

MACHINE LEARNING

CODE 98795
ACADEMIC YEAR 2022/2023
CREDITS
  • 6 cfu during the 3nd year of 8766 STATISTICA MATEM. E TRATTAM. INFORMATICO DEI DATI (L-35) - GENOVA
  • 6 cfu during the 1st year of 9011 MATEMATICA(LM-40) - GENOVA
    		•
    SCIENTIFIC DISCIPLINARY SECTOR MAT/06
    TEACHING LOCATION
  • GENOVA
    		•
    SEMESTER 2° Semester
    TEACHING MATERIALS AULAWEB

    OVERVIEW

    The course  offers an introduction to the main machine learning algorithms, by covering the modeling and computational aspects.

    AIMS AND CONTENT

    LEARNING OUTCOMES

    The primary objective is to provide the students with the basic language and tools of machine learning, with particular emphasis on the supervised case. The approach is based on a formulation of the problem of machine learning as an inverse stochastic problem. The students will also need to know some of the best known algorithms, including both statistical and computational properties.

    AIMS AND LEARNING OUTCOMES

    At the end of the course, the student will have:

    • a good understanding of the basic notions of machine learning and of the related basic mathematical tools; 
    • a comprehension  of the basic concepts and techniques of convex optimization
    • a good knowledge of the statistical and computational  properties of some well known machine learning   algorithms;
    • some ability to implement machine learning algorithms on synthetic and real data sets.

    PREREQUISITES

    Calculus for functions of sesveral variables, probability and linear algebra. 

    TEACHING METHODS

    Classes using  blackboard and lab activities

    SYLLABUS/CONTENT

    The course will offer an introduction to the main tools which are necessary to understand statistical learning,  and a number of supervised learning algorithms, such as local methods, regularization networks, linear and non linear models. The Course will also give a basic introduction to neural networks. Unsupervised problems such as clustering and dimensionality reduction will also be treated.  All the methods covered in the course will be implemented and during the lab sessions. 

    RECOMMENDED READING/BIBLIOGRAPHY

    TEACHERS AND EXAM BOARD

    Exam Board

    SILVIA VILLA (President)

    CESARE MOLINARI

    LORENZO ROSASCO (President Substitute)

    ERNESTO DE VITO (Substitute)

    LESSONS

    LESSONS START

    In agreement with the offical academic calendar

    Class schedule

    MACHINE LEARNING

    EXAMS

    EXAM DESCRIPTION

    To pass the exam the student have to present a short report. The student can choose one among the following options:

    1. analyze and discuss a research article on themse close to the ones studied during classes
    2. implement an algorithms presented during the classes (in some programming language)
    3. use some available code to analyze synthetic and/or real datasets and discuss the obtained results.

    The topic studied in the report must be decided in advance in agreement with the instructors.


    Students with DSA certification ("specific learning disabilities"), disability or other special educational needs are advised to contact the teacher at the beginning of the course to agree on teaching and examination methods that, in compliance with the teaching objectives, take account of individual learning arrangements and provide appropriate compensatory tools.

    ASSESSMENT METHODS

    The report preparation and its discussion are aimed at verifying the student's achievement of an independent critical reasoning capability  in the context of machine learning.

    During the report presentation, basic questions d on the topics covered by the course amy be asked.