Caricamento in corso...
Spectral and machine learning methods for uncertainty quantification
01UJCRV
A.A. 2019/20
Course Language
Inglese
Degree programme(s)
Doctorate Research in Ingegneria Elettrica, Elettronica E Delle Comunicazioni - Torino
Course structure
| Teaching | Hours |
|---|---|
| Lezioni | 21 |
Lecturers
| Teacher | Status | SSD | h.Les | h.Ex | h.Lab | h.Tut | Years teaching |
|---|---|---|---|---|---|---|---|
| Trinchero Riccardo | Professore Associato | IIET-01/A | 9 | 0 | 0 | 0 | 4 |
Co-lectures
Context
| SSD | CFU | Activities | Area context | *** N/A *** |
|---|
2019/20
PERIOD: MARCH - APRIL
This course will address the application of two classes of techniques for the uncertainty quantification, that became subject of intensive research in the last decade, namely, spectral methods based on the so-called “polynomial chaos” framework, and Machine Learning techniques, such as Support Vector Machines and Gaussian Process regression. The lessons will cover the main theoretical notions and implementational details. A rigorous mathematical framework is complemented by illustrative examples. At the end of the course, the attendees are expected to be acquainted with the tools and capable of independently apply them to relevant problems in their respective fields of application.
PERIOD: MARCH - APRIL
This course will address the application of two classes of techniques for the uncertainty quantification, that became subject of intensive research in the last decade, namely, spectral methods based on the so-called “polynomial chaos” framework, and Machine Learning techniques, such as Support Vector Machines and Gaussian Process regression. The lessons will cover the main theoretical notions and implementational details. A rigorous mathematical framework is complemented by illustrative examples. At the end of the course, the attendees are expected to be acquainted with the tools and capable of independently apply them to relevant problems in their respective fields of application.
Introduction (motivations and definitions). Monte Carlo method. The polynomial chaos expansion and orthogonal polynomials. Stochastic Galerkin method. Stochastic collocation method. Metamodels. Least-square regression method. Stochastic testing method. Global sensitivity analysis. Correlated random parameters. Support Vector Machine and Least-Squares Support Vector Machine regression. Deterministic vs. probabilistic models. Gaussian process regression.
Introduction (motivations and definitions). Monte Carlo method. The polynomial chaos expansion and orthogonal polynomials. Stochastic Galerkin method. Stochastic collocation method. Metamodels. Least-square regression method. Stochastic testing method. Global sensitivity analysis. Correlated random parameters. Support Vector Machine and Least-Squares Support Vector Machine regression. Deterministic vs. probabilistic models. Gaussian process regression.
- MON 14/09/2020 14:00 - 16:30 (Manfredi)
- WED 16/09/2020 14:00 - 16:30 (Manfredi)
- FRI 18/09/2020 14:00 - 16:30 (Manfredi)
- MON 21/09/2020 14:00 - 16:30 (Manfredi) - corso di Chicco-Piglione (DENERG) tutto il giorno.
- FRI 25/09/2020 14:00 - 16:30 (Manfredi)
- MON 28/09/2020 14:00 - 17:00 (Trinchero)
- WED 30/09/2020 14:00 - 17:00 (Trinchero)
- FRI 02/10/2020 14:00 - 17:00 (Trinchero)
- MON 14/09/2020 14:00 - 16:30 (Manfredi)
- WED 16/09/2020 14:00 - 16:30 (Manfredi)
- FRI 18/09/2020 14:00 - 16:30 (Manfredi)
- MON 21/09/2020 14:00 - 16:30 (Manfredi) - corso di Chicco-Piglione (DENERG) tutto il giorno.
- FRI 25/09/2020 14:00 - 16:30 (Manfredi)
- MON 28/09/2020 14:00 - 17:00 (Trinchero)
- WED 30/09/2020 14:00 - 17:00 (Trinchero)
- FRI 02/10/2020 14:00 - 17:00 (Trinchero)
Modalità di esame:
Exam:
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Gli studenti e le studentesse con disabilità o con Disturbi Specifici di Apprendimento (DSA), oltre alla segnalazione tramite procedura informatizzata, sono invitati a comunicare anche direttamente al/la docente titolare dell'insegnamento, con un preavviso non inferiore ad una settimana dall'avvio della sessione d'esame, gli strumenti compensativi concordati con l'Unità Special Needs, al fine di permettere al/la docente la declinazione più idonea in riferimento alla specifica tipologia di esame.
Exam:
In addition to the message sent by the online system, students with disabilities or Specific Learning Disorders (SLD) are invited to directly inform the professor in charge of the course about the special arrangements for the exam that have been agreed with the Special Needs Unit. The professor has to be informed at least one week before the beginning of the examination session in order to provide students with the most suitable arrangements for each specific type of exam.