PhD Course: Advanced Machine Learning

Published October 5, 2026 - 12:12

Machine Learning and Deep Learning have become essential tools for solving complex problems and driving progress in science, technology, and industry. This course offers a comprehensive introduction to the principles and advanced techniques of Machine Learning (ML) and Deep Learning (DL), with a special focus on representation learning. It starts by introducing the core concepts of supervised and unsupervised learning, probabilistic modeling, and the distinction between generative and discriminative models. The focus then shifts to the fundamentals of neural networks, including architectures, optimization strategies, backpropagation, and essential regularization techniques. The course further explores key representation learning methods like autoencoders, variational autoencoders (VAEs), and generative adversarial networks (GANs), exploring both their theoretical foundations and practical applications. The program concludes by highlighting current and emerging trends, along with real-world applications across different domains. Participants will acquire both solid theoretical understanding and practical skills in modern ML and DL methodologies.

Orario corso:

  • Mercoledì 07/10/2026 9:00-12:00
  • Giovedì 08/10/2026 9:00-12:00
  • Venerdì 09/10/2026 9:00-11:00
  • Lunedì 12/10/2026 15:00-17:00
  • Martedì 13/10/2026 9:00-11:00

Le lezioni si terranno presso l'Aula Tecnica DIMES (Cubo 41C, I° piano)

 

Teachers: 
Giuseppe Manco
Luca Ferragina