Machine Learning & AI Methods - Theory, Techniques, and Advanced Engineering Applications

Name (Acronym)

Machine Learning & AI Methods – Theory, Techniques, and Advanced Engineering Applications (MLAI)
Date

03 November - 14 November 2025
(3 weeks, 20 hours/week)
Teacher

BROCCARDO Marco, SALUCCI Marco
Contact E-mail

2025.PHD.MLAI.UniTN.TRENTO.IT@eledia.org
ECTS

4
Syllabus

ESSE3 Page
Institution

ICAM PhD School
Study Program

PhD in Civil, Environmental and Mechanical Engineering [ICAM]
Degree

Ph.D.
Language

English
Tracks

EM Vision and IA‐Based Diagnostics, Fields and Waves ("Advanced EM Technologies")

ABSTRACT

Understanding and solving complex problems in the physical world has been an intelligent endeavor of humankind. Moreover, the study of artificial intelligence (AI) embodies the dream of designing machines like humans. Research in machine learning (ML) and, more recently, on deep learning (DL) techniques has attracted much attention in many engineering fields. With the spreading of such techniques, improvement in learning capacity may allow machines to “learn” from a large amount of physical data and “master” the physical laws in certain controlled boundary conditions. In the long run, a hybridization of fundamental physical principles with “knowledge” from big data could unleash numerous engineering applications that used to be impossible due to the limit of data information and computational capabilities. The course aims at providing a solid background knowledge on AI and ML, with a focus on recent and competitive methodologies for the efficient and robust solution of both classification and regression problems in advanced engineering applications. Applicative examples including exercises will corroborate the theoretical concepts.

COURSE FORMAT

The Course is taught in 🇬🇧️ ENGLISH and offered

On-site
On-line (synchronous and asynchronous)

with video recordings, hand-outs, etc. of the lectures available off-line. (*)

COURSE CONTENT

Basics, fundamental theory, and pillar concepts of AI and ML
ML as a "three-steps" learning process
Feature selection and feature extraction strategies for dimensionality reduction
Single-shot and adaptive sampling strategies
ML techniques for classification tasks
ML techniques for regression tasks
Basics of Deep Learning
Applicative examples including exercises regarding specific engineering applications of AI and ML

TEACHING ACTIVITIES

Theoretical Lessons
e-Xam Self Assessment (each teaching class or periodically)
MATLAB Hands-On
e-Xam Final Assessment

FURTHER READINGS

A. I. J. Forrester, A. Sobester, and A. J. Keane, Engineering Design via Surrogate Modelling: A Practical Guide. Hoboken, N.J.: John Wiley & Sons, 2008.
A. Massa, G. Oliveri, M. Salucci, N. Anselmi, and P. Rocca, “Learning-by-examples techniques as applied to electromagnetics,” J. Electromagn. Waves Appl., vol. 32, no. 4, pp. 516-541, 2018
M. Salucci and M. Li, Eds., Applications of Deep Learning in Electromagnetics - Teaching Maxwell's Equations to Machines. London, United Kingdom: IET, 2023.

(*) Each registered participant acknowledges that the material distributed in the frame of the course, available for the duration of one academic year, is protected by copyright and delivered for educational purposes and personal use only. The participant agrees and undertakes not to forward, publish, disclose, distribute, disseminate - in any form or manner - such a material without written consent of the author(s) of the material. Unless otherwise explicitly allowed by the speaker in written form, no recordings of the online lectures can be made.