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Name (Acronym)Machine Learning & AI Methods – Theory, Techniques, and Advanced Engineering Applications (MLAI)
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ECTS4
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Contact E-maildidattica@eledia.org
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Teacher
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Syllabus
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Institution
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Degree
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LanguageEnglish
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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 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.
Course Information
| Date: | 25th November - 13rd December 2024 (1 week, 32 hours) |
| Format: | The course is offered in blended form (onsite and online) |
Registration Information
Enrollment deadline: 14th November 2024 (onsite); 21st November 2024 (online).
| UniTN Students (*): | Free |
| EXTERNAL Students: |
400 Eu: Onsite attendance 200 Eu: Online attendance |
The fees include the course teaching, the slides/material, and the video recordings (**).
(*) Student status certification of the University of TRENTO is required
Registration Procedure
Complete the online application through the decicated registration webpage.
NOTES:
- 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.