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

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

  1. 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.
  2. 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
  3. M. Salucci and M. Li, Eds., Applications of Deep Learning in Electromagnetics - Teaching Maxwell's Equations to Machines. London, United Kingdom: IET, 2023.