First cycle
degree courses
Second cycle
degree courses
Single cycle
degree courses
School of Engineering
AUTOMATION ENGINEERING
Course unit
LEARNING DYNAMICAL SYSTEMS
INP7080354, A.A. 2018/19

Information concerning the students who enrolled in A.Y. 2017/18

Information on the course unit
Degree course Second cycle degree in
AUTOMATION ENGINEERING
IN0527, Degree course structure A.Y. 2008/09, A.Y. 2018/19
N0
bring this page
with you
Number of ECTS credits allocated 9.0
Type of assessment Mark
Course unit English denomination LEARNING DYNAMICAL SYSTEMS
Department of reference Department of Information Engineering
E-Learning website https://elearning.dei.unipd.it/course/view.php?idnumber=2018-IN0527-000ZZ-2017-INP7080354-N0
Mandatory attendance No
Language of instruction English
Branch PADOVA
Single Course unit The Course unit can be attended under the option Single Course unit attendance
Optional Course unit The Course unit can be chosen as Optional Course unit

Lecturers
Teacher in charge MATTIA ZORZI ING-INF/04

ECTS: details
Type Scientific-Disciplinary Sector Credits allocated
Core courses ING-INF/04 Automatics 9.0

Course unit organization
Period First semester
Year 2nd Year
Teaching method frontal

Type of hours Credits Teaching
hours
Hours of
Individual study
Shifts
Lecture 9.0 72 153.0 No turn

Calendar
Start of activities 01/10/2018
End of activities 18/01/2019

Examination board
Board From To Members of the board
1 A.A. 2018/2019 01/10/2018 15/03/2020 ZORZI MATTIA (Presidente)
FERRANTE AUGUSTO (Membro Effettivo)
BEGHI ALESSANDRO (Supplente)
BISIACCO MAURO (Supplente)
CARLI RUGGERO (Supplente)
CENEDESE ANGELO (Supplente)
CHIUSO ALESSANDRO (Supplente)
PILLONETTO GIANLUIGI (Supplente)
PINZONI STEFANO (Supplente)
SCHENATO LUCA (Supplente)
SUSTO GIAN ANTONIO (Supplente)
TICOZZI FRANCESCO (Supplente)
VALCHER MARIA ELENA (Supplente)
ZAMPIERI SANDRO (Supplente)

Syllabus
Prerequisites: The course requires the basic knowledge of: signals and systems, data analysis, systems theory, linear algebra.
Target skills and knowledge: At the end of the course the student will have developed the basic knowledge on the following aspects of Systems Engineering:
- Knowledge of the principal methods for learning dynamic systems
- Ability of applying the principal methods for learning dynamic systems
- Ability of using the Matlab toolbox “System Identification”
Examination methods: The expected knowledge and abilities are checked through an exam divided in three parts: theory, lab and presentation. In the theoretical part the student takes a written test which contains 3 or 4 questions. A question can regard a proof or an exercise saw in the lectures or a slight modification of them. In the lab part the student must do 5 homeworks, each of them corresponds to a lab lecture. In each homework the student must develop a Matlab code and answer to some questions pertinent to the lab lecture. The last part of the exam is not mandatory and is conducted in groups of two, three or four students. Each group have to study and then present a research paper given by the instructor and pertinent to the topics of the course.
Assessment criteria: The abilities and knowledge acquired are verified through the following assessment criteria:
- Completeness of the acquired knowledge
- Exactness of the logical steps to argue an answer
- Correct use of technical terminology
- Correct use of the learned methods in contexts which are different from the ones saw in the course
Course unit contents: Fisherian Static Estimation;
Bayesian Static Estimation;
Background on Dynamic Models;
PEM method;
Kernel-based PEM method;
Recursive Methods;
Model Structure Determination
Planned learning activities and teaching methods: The learning activities are constituted by traditional lessons which address the contents of the course and by 5 labs in which the different methods saw in the lessons are applied to real and synthetic data.
Additional notes about suggested reading: All the material presented in the lectures is available in the moodle platform.
Textbooks (and optional supplementary readings)
  • James G., Witten D., Hastie T., and Tibshirani R., An introduction to statistical learningwith applications in R. New York [etc.]: Springer, 2013. Cerca nel catalogo
  • Rasmussen, Carl Edward; Williams, Christopher K.I., Gaussian processes for machine learningCarl Edward Rasmussen, Christopher K.I. Williams. Cambridge: Mass., MIT Press, 2006. Cerca nel catalogo
  • Papoulis, Athanasios; Pillai, Unnikrishna S., Probability, random variables, and stochastic processes. Boston [etc.]: McGraw-Hill, 1991. Cerca nel catalogo
  • Chen T., Ohlsson H., Ljung L., On the estimation of transfer functions, regularizations and Gaussian processes-Revisited. --: Automatica n.48, 1525-1535, 2012. Cerca nel catalogo
  • Petersen K., Pedersen, M., The matrix cookbook. --: Technical University of Denmark, Vol. 7, 2008. Cerca nel catalogo

Innovative teaching methods: Teaching and learning strategies
  • Laboratory
  • Working in group
  • Loading of files and pages (web pages, Moodle, ...)

Innovative teaching methods: Software or applications used
  • Moodle (files, quizzes, workshops, ...)
  • Latex
  • Matlab

Sustainable Development Goals (SDGs)
Quality Education