|
|
| Academic Year |
2024/2025 |
| Name |
Dott. - MI (1380) Ingegneria dell'Informazione / Information Technology |
| Programme Year |
1 |
| ID Code |
062782 |
| Course Title |
DATA-DRIVEN APPROACHES TO UNCERTAIN OPTIMIZATION AND DECISION-MAKING: THEORY AND APPLICATIONS |
| Course Type |
MONO-DISCIPLINARY COURSE |
| Credits (CFU / ECTS) |
5.0 |
| Course Description |
Designing in presence of uncertainty is ubiquitous in science and engineering and instances of problems can be found in a large variety of application domains, ranging from robust and predictive control to estimation and prediction, from management to quantitative finance, from operation research to machine learning to mention a few. The major challenge nowadays is that the complexity of the problems of interest has somehow surpassed the available domain knowledge and ever more often traditional, model-based, approaches fall short in providing satisfactory solutions. In this course, the students will be introduced to sample-based methods for uncertain optimization, where uncertainty is described by means of a finite number of cases drawn from the, typically infinite, set of possible uncertainty outcomes. Samples can as well be observations, and this covers data-driven approaches to decision-making in general. Particular emphasis will be given to the scenario approach, which is a key methodology in this context to obtain valid solutions in a variety of optimization problems involving uncertainty. Central to the course is an in-depth presentation of the powerful generalization theory that has been developed within the framework of the scenario approach. It will be shown how these results allow one to rigorously certify solutions, which otherwise would be heuristic, and, therefore, to fully exploit the information content of data. Applications to control, prediction theory and machine learning will be discussed as well. In the course, special attention will be given to a precise mathematical formulation of the problems and to a detailed presentation of the ensuing results. Practical examples will illustrate the ideas.
Keywords: Optimization in presence of uncertainty, Data-driven decision-making, Learning-based methods, Scenario approach, Discussion of open problems that offer an opportunity for research. |
| Scientific-Disciplinary Sector (SSD)
|
|
SSD Code
|
SSD Description
|
CFU
|
|
ING-INF/04
|
SYSTEMS AND CONTROL ENGINEERING
|
5.0
|
|
|
Alphabetical group
|
Name
|
Teaching Assignment Details
|
|
From (included)
|
To (excluded)
|
|
A
|
ZZZZ
|
Garatti Simone, Campi Marco Claudio
|
|
|
