First cycle
degree courses
Second cycle
degree courses
Single cycle
degree courses
School of Engineering
MATHEMATICAL ENGINEERING
Course unit
SYSTEM IDENTIFICATION AND DATA ANALYSIS
INP8084399, A.A. 2019/20

Information concerning the students who enrolled in A.Y. 2019/20

Information on the course unit
Degree course Second cycle degree in
MATHEMATICAL ENGINEERING
IN2191, Degree course structure A.Y. 2017/18, A.Y. 2019/20
N0
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Degree course track Common track
Number of ECTS credits allocated 9.0
Type of assessment Mark
Course unit English denomination SYSTEM IDENTIFICATION AND DATA ANALYSIS
Department of reference Department of Civil, Environmental and Architectural Engineering
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
No lecturer assigned to this course unit

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

Course unit organization
Period Second semester
Year 1st 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 02/03/2020
End of activities 12/06/2020
Show course schedule 2019/20 Reg.2017 course timetable

Syllabus
Prerequisites: None
Target skills and knowledge: Objective
Introduce the students to the advanced topics of linear algebra and model identification.

Outcomes
A student who has met the objectives of the course will have a fundamental knowledge of:

• Linear algebra and numerical methods for large sparse matrices
• Deterministic and stochastic methods for model identification and calibration.
Examination methods: Written with optional computer project. Two Partial tests during the academic year plus four regular exam sessions
Assessment criteria: Completeness and orderliness of essay; clarity of exposition; rigour in using the technical terminology. The level of correspondence to these criteria will determine the graduation of the judgement and, consequently, the final mark.
Course unit contents: 1. Review of linear algebra concepts;
2. Iterative methods for the solution of large, sparse linear systems: a) conjugate gradient methods for symmetric systems; b) projection methods for nonsymmetric systems (GMRES-BiCGSTAB); c) preconditioning; incomplete factorizations; sparse factorized approximate inverses; d) implementation techniques; sparse (CSR) matrix storage;
3. Methods for the calculation of eigenvalues and eigenvectors: a) Power and inverse power (with shift) methods; b) QR method.
4. Newton methods for nonlinear systems: a) derivation of the Newton methods; b) local convergence properties and introduction to globalization techniques; c) Picard method; d) implementation of the Newton-Krylov and inexact Newton methods.
5. The calibration as an ill posed problem;
6. Penalizing functions;
7. Likelihood method for estimation;
8. Generalized Method of Moments;
9. Deterministic and stochastic algorithms.
Planned learning activities and teaching methods: Frontal teaching and practical exercises, that the student has to further develop and deepen with his study.
Additional notes about suggested reading: Classroom notes: STATISTICAL METHODS FOR
DYNAMICAL DATA ANALYSIS
by Giorgio Picci
Textbooks (and optional supplementary readings)
  • Hastie Tibshirani Friedman, The Elements of Statistical Learning. --: Springer, 2009. Cerca nel catalogo