Salta al contenuto principale della pagina

MACHINE LEARNING AND DATA ANALYSIS

CODE 86798
ACADEMIC YEAR 2023/2024
CREDITS
  • 6 cfu during the 1st year of 10852 COMPUTER SCIENCE (LM-18) - GENOVA
  • 6 cfu during the 2nd year of 11160 COMPUTER ENGINEERING (LM-32) - 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 11661 DIGITAL HUMANITIES - INTERACTIVE SYSTEMS AND DIGITAL MEDIA(LM-92) - GENOVA
  • 9 cfu during the 1st 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.

    During the course the following skills will be developed
    - personal competence
    - social competence
    - ability to learn to learn
    - competence in project creation
    - competence in project management

    PREREQUISITES

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

    TEACHING METHODS

    - Frontal lesson (approx. 50% to develop ability to learn to learn)
    - Laboratories (approx. 50% to develop personal competence)
    - Possibility of a final project in pairs (to develop social competence, competence in project creation, and competence in project management)

    For working students and students with certification of Specific Learning Disabilities (SLD), disabilities, or other special educational needs are advised to contact the instructor at the beginning of the course to arrange teaching and examination methods that, while respecting the teaching objectives, take into account individual learning styles.

    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
    C. C. Aggarwal "Neural networks and deep learning." 2023
    L. Oneto "Model Selection and Error Estimation in a Nutshell" 2020

    TEACHERS AND EXAM BOARD

    LESSONS

    LESSONS START

    https://easyacademy.unige.it/portalestudenti/index.php?view=easycourse&_lang=it&include=corso

    Class schedule

    All class schedules are posted on the EasyAcademy portal.

    EXAMS

    EXAM DESCRIPTION

    Oral by appointment.

    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