logo-polimi
Loading...
Risorse bibliografiche
Risorsa bibliografica obbligatoria
Risorsa bibliografica facoltativa
Scheda Riassuntiva
Anno Accademico 2019/2020
Scuola Scuola di Ingegneria Industriale e dell'Informazione
Insegnamento 052351 - MODEL IDENTIFICATION AND DATA ANALYSIS
  • 052349 - STATISTICAL LEARNING FOR AUTOMATION SYSTEMS
Docente Formentin Simone
Cfu 5.00 Tipo insegnamento Modulo Di Corso Strutturato

Corso di Studi Codice Piano di Studio preventivamente approvato Da (compreso) A (escluso) Insegnamento
Ing Ind - Inf (Mag.)(ord. 270) - MI (473) AUTOMATION AND CONTROL ENGINEERING - INGEGNERIA DELL'AUTOMAZIONE*AZZZZ052351 - MODEL IDENTIFICATION AND DATA ANALYSIS
Ing Ind - Inf (Mag.)(ord. 270) - MI (476) ELECTRONICS ENGINEERING - INGEGNERIA ELETTRONICA*AZZZZ052351 - MODEL IDENTIFICATION AND DATA ANALYSIS
Ing Ind - Inf (Mag.)(ord. 270) - MI (486) ENGINEERING PHYSICS - INGEGNERIA FISICA*AZZZZ052351 - MODEL IDENTIFICATION AND DATA ANALYSIS

Obiettivi dell'insegnamento

The goal of the course is to enable students to extract (“learn”) from measured data useful information for automation, systems and control applications. The course covers the fundamentals of data-driven learning, as well as a series of methods for linear and nonlinear static systems modeling, clustering, time series characterization and virtual sensing in dynamical systems.  Each technique is treated both theoretically and practically through computer exercises with real-world data.


Risultati di apprendimento attesi

Through theoretical lectures and computer sessions, the students are expected to:

  • understand the fundamental problems, shared by applied statistics, machine learning and system identification, that can be encountered while extracting information from a finite set of observations taken from an unknown system;
  • learn the main tools to avoid the most common problems in data-driven modeling, e.g., overfitting;
  • be able to formulate learning problems corresponding to different applications;
  • understand a range of statistical learning algorithms along with their strengths and weaknesses;
  • be able to apply learning algorithms to solve application problems;
  • be able to read current research papers and understand the issues raised by current research in the field.

Argomenti trattati

The program of the course is the following.

  • Introduction to statistical learning: main definitions and comparison with similar disciplines
  • The mathematical foundations of learning
    • Feasibility of statistical learning
    • Approximation VS generalization
  • Learning of static models
    • Linear regression
    • Logistic regression
    • Linear classification
    • Overfitting: regularization and validation
    • Neural networks
  • Data-preprocessing
    • Input pre-processing and data-cleaning
    • Dimensionality reduction and feature selection
  • Clustering
  • Towards learning of dynamical models
    • Stochastic processes
    • Data-preprocessing for time-series
  • Virtual sensing: the Kalman filter

Prerequisiti
  • Familiarity with basic concepts of computer science (algorithms and complexity) and dynamical systems theory.
  • Mathematical maturity in linear algebra and probability theory.

Modalità di valutazione

The exam will be a written test in one of the official available dates. The exam will consist of both theoretical questions and simple (i.e., no computer-aided) exercises. The final marks will be awarded based on correctness of the answers, appropriateness of technical terminology and clarity of exposition. Specifically, the students will be asked to:

  • show their understanding of the main problems (and the corresponding countermeasures) in the field of data-driven modeling and learning in general;
  • select and describe the most appropriate learning tools for a given application;
  • use analytical methods to solve learning problems of moderate complexity.

Bibliografia
Risorsa bibliografica obbligatoriaSimone Formentin, Lecture notes and Python exercise notebooks
Risorsa bibliografica facoltativaYaser S. Abu-Mostafa, Malik Magdon-Ismail, Hsuan-Tien Lin, Learning from data, Editore: AML Book, Anno edizione: 2012
Risorsa bibliografica facoltativaGareth James, Daniela Witten, Trevor Hastie, Robert Tibshirani., An Introduction to Statistical Learning: With Applications in R, Editore: Springer, Anno edizione: 2013 http://www-bcf.usc.edu/~gareth/ISL/
Note:

Freely available online.

Risorsa bibliografica facoltativaMohinder S. Grewal, Angus P. Andrews, Kalman Filtering: Theory and Practice Using MATLAB, Editore: Wiley Online Library, Anno edizione: 2008 http://read.pudn.com/downloads148/ebook/638857/Kalman%20Filtering%20-%20Theory%20and%20Practice%20Using%20MATLAB,%203rd%20Ed.pdf
Note:

Freely available online


Forme didattiche
Tipo Forma Didattica Ore di attività svolte in aula
(hh:mm)
Ore di studio autonome
(hh:mm)
Lezione
30:00
45:00
Esercitazione
20:00
30:00
Laboratorio Informatico
0:00
0:00
Laboratorio Sperimentale
0:00
0:00
Laboratorio Di Progetto
0:00
0:00
Totale 50:00 75:00

Informazioni in lingua inglese a supporto dell'internazionalizzazione
Insegnamento erogato in lingua Inglese
Disponibilità di materiale didattico/slides in lingua inglese
Disponibilità di libri di testo/bibliografia in lingua inglese
Possibilità di sostenere l'esame in lingua inglese
Disponibilità di supporto didattico in lingua inglese
schedaincarico v. 1.6.1 / 1.6.1
Area Servizi ICT
26/01/2020