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MACHINE LEARNING

CODE 98959
ACADEMIC YEAR 2021/2022
CREDITS
  • 6 cfu during the 2nd year of 9269 INGEGNERIA MECCANICA - PROGETTAZIONE E PRODUZIONE(LM-33) - LA SPEZIA
  • SCIENTIFIC DISCIPLINARY SECTOR INF/01
    LANGUAGE English
    TEACHING LOCATION
  • LA SPEZIA
  • SEMESTER 1° Semester
    TEACHING MATERIALS AULAWEB

    OVERVIEW

    The course provides basic knowledge of classic and modern machine learning techniques, that can fruitfully be applied to disparate fields such as production line automation, quality monitoring, robotics, surveillance, self-driving vehicles, among many others.

    AIMS AND CONTENT

    LEARNING OUTCOMES

    This course provides an introduction to machine learning and statistical pattern recognition. Topics include: (1) Pattern recognition basics and theory. (2) Supervised learning ideas and methods. (3) Unsupervised learning ideas and some relevant methods. (4) Machine learning workflows and best practices. The course will also cover relevant success stories, and possible applications and case studies in the fields of robotics and smart industrial automation

    AIMS AND LEARNING OUTCOMES

    After successfully attending the course, the student will be able to:

    • demonstrate knowledge of a range of techniques and problems in machine learning and pattern recognition, including the underlying scientific and technical rationale
    • apply selected techniques to relevant problems
    • code simple and medium-complexity machine learning methods using standard programming tools, without being limited to using software libraries
    • tackle the workflow of a machine learning assignment from data wrangling to result presentation
    • use critical thinking to analyse a problem and select the appropriate machine learning method to apply

    PREREQUISITES

    • Basic knowledge of calculus, linear algebra, geometry, which are typically acquired during the first or second year in any engineering curriculum
    • Basic, but operational, knowledge of Matlab or Python programming

    More basic topics (elements of probability, of statistics, of optimisation) will be covered during the course

    TEACHING METHODS

    Lectures, guided labs, homework assignments

    SYLLABUS/CONTENT

    • Introduction, basic concepts, types of problems
    • Linear thershold classifiers
    • Probabilities; Bayesian decision theory (the Naive bayes classifier)
    • Linear regression as a simple learning problem
    • Optimisation (convexity, criteria, gradient descent, stochastic methods)
    • Statistics and learning (sampling, parameter estimation)
    • Parametric and non-parametric methods (Gaussian mixtures, nearest neighbour rules, decision trees/forests)
    • Evaluation of classifiers (methodology, quality indices)
    • Neural networks (history, perceptrons, multilayer perceptrons, the error back-propagation algorithm, deep learning)
    • Unsupervised learning (clustering methods)
    • Mapping and input space transformations (PCA, nonlinear embedding methods, kernel methods, support vector machines)

    RECOMMENDED READING/BIBLIOGRAPHY

    Course slides/handouts

    For a detailed bibliograpy please refer to the course Aulaweb page (from https://corsi.unige.it/9269#chapter-5 open Manifesto degli Studi, look up Machine Learning and click it)

    TEACHERS AND EXAM BOARD

    Exam Board

    STEFANO ROVETTA (President)

    FRANCESCO MASULLI (President Substitute)

    LESSONS

    Class schedule

    All class schedules are posted on the EasyAcademy portal.

    EXAMS

    EXAM DESCRIPTION

    Written quiz, homework assignments

    ASSESSMENT METHODS

    Evaluation of homeworks (50%)

    Quiz grading (50%)

    Exam schedule

    Date Time Location Type Notes
    10/01/2022 14:00 LA SPEZIA Scritto + Orale
    08/02/2022 14:00 LA SPEZIA Scritto + Orale
    17/06/2022 14:00 LA SPEZIA Scritto + Orale
    30/06/2022 14:00 LA SPEZIA Scritto + Orale
    06/09/2022 14:00 LA SPEZIA Scritto + Orale