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MACHINE LEARNING AND DATA ANALYSIS

CODE 86798
ACADEMIC YEAR 2022/2023
CREDITS
  • 6 cfu during the 1st year of 10852 COMPUTER SCIENCE (LM-18) - GENOVA
  • 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 11160 COMPUTER ENGINEERING (LM-32) - GENOVA
  • 6 cfu during the 2nd year of 11160 COMPUTER ENGINEERING (LM-32) - GENOVA
  • 3 cfu during the 2nd year of 10635 ROBOTICS ENGINEERING (LM-32) - GENOVA
    		•
    SCIENTIFIC DISCIPLINARY SECTOR ING-INF/05
    LANGUAGE English
    TEACHING LOCATION
  • GENOVA
    		•
    SEMESTER 1° Semester
    TEACHING MATERIALS AULAWEB

    OVERVIEW

    In the information age any system or device generates some form of data for diagnostic purposes or analysis.
    he course details the techniques for analyzing data in order to extract useful information and knowledge for decision making.

    AIMS AND CONTENT

    LEARNING OUTCOMES

    Students will be provided with advanced skills related to machine learning and data analysis.
    Students will learn insights on machine learning and data analysis methodologies and a series of real world applications.

    AIMS AND LEARNING OUTCOMES

    The student will be able to apply the acquired skills to a case study by deriving the model of the phenomenon that generated the data under analysis.

    PREREQUISITES

    Coding (Matlab/Python/R), linear algebra, probability and statistics.

    TEACHING METHODS

    The course consists of lectures and practical lab sessions using Matlab/Python/R

    SYLLABUS/CONTENT

    1. Statistical inference
    2. Supervised, Semisupervised, and Unsupervised Learning
    3. Statistical Learning Theory
    4. Shallow Machine Learning Algorithms (examples of coding in Matlab/Python/R)
    5. Deep Machine Learning Algorithms (examples of coding in Matlab/Python/R)
    6. Model Selection and Error Estimation

    RECOMMENDED READING/BIBLIOGRAPHY

    C. C. Aggarwal "Data Mining - The textbook" 2015
    T. Hastie, R.Tibshirani, J.Friedman "The Elements of Statistical Learning: Data Mining, Inference, and Prediction" 2009.
    S. Shalev-Shwartz, S. Ben-David "Understanding machine learning: From theory to algorithms" 2014
    I. Goodfellow, Y. Bengio, A. Courville "Deep learning" 2016
    L. Oneto "Model Selection and Error Estimation in a Nutshell" 2020

    TEACHERS AND EXAM BOARD

    Exam Board

    LUCA ONETO (President)

    MARCO MARATEA

    DAVIDE ANGUITA (President Substitute)

    LESSONS

    LESSONS START

    https://courses.unige.it/11160/p/students-timetable

    Class schedule

    All class schedules are posted on the EasyAcademy portal.

    EXAMS

    EXAM DESCRIPTION

    Oral by appointment.

    Students with learning disorders ("disturbi specifici di apprendimento", DSA) will be allowed to use specific modalities and supports that will be determined on a case-by-case basis in agreement with the delegate of the Engineering courses in the Committee for the Inclusion of Students with Disabilities.

    ASSESSMENT METHODS

    The student will solve a real problem at will by applying the techniques learned during the course.

    Exam schedule

    Date Time Location Type Notes
    16/02/2023 09:00 GENOVA Esame su appuntamento
    15/09/2023 09:00 GENOVA Esame su appuntamento