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Name (Acronym)Machine Learning & AI Methods – Theory, Techniques, and Advanced Engineering Applications (MLAI)
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Date03 November - 14 November 2025
(2 weeks, 30 hours/week) -
Teacher
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Contact E-mail2025.PHD.MLAI.UniTN.TRENTO.IT@eledia.org
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ECTS4
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Syllabus
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Institution
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Study Program
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Degree
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LanguageEnglish
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Tracks
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.
For further references please contact the Teacher(s).
(*) 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.
Registration Information
| UniTN Students: | Free |
| EXTERNAL Students: |
216 Eu: First course 180 Eu: Every course from the second one |
The fees include the course teaching, video recordings, hand-outs, etc. (*).
Registration Procedure for UniTN Students
Please contact the Student Support Office of your Department/Centre/School to include the course in your study plan.
Registration Procedure for EXTERNAL Students
Step 1: Register a "guest" type account (@guest.unitn.it)
- Should you still not have a UniTN account, you have to register and log in with your SPID identity or CIE (electronic ID card). If you cannot use SPID or CIE, please create your own UniTN account.
Step 2: Enroll to a Single UniTN Course
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Complete the online application through the dedicated webpage.
In the application form (Section "Teaching Activities") put the following information:- Name of single class/teaching activity: Artificial Intelligence and Machine Learning Methods for Environmental Applications
- Code of single class/teaching activity: 140711
- Degree course to which the teaching activity is associated: [0332H] Ingegneria per l’Ambiente e il Territorio
- Once received the outcome of the application (1-3 days), login into ESSE3 with your "guest" account user-name and password. Then, pay the bulletin you find in Administrative Office – Payments.
NOTES:
- A vademecum with a step-by-step guide to enroll to a single course at the University of Trento is available here
- For any question on the registration process, please write to didattica@eledia.org
(*) 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.