DEEP LEARNING

The aim of the course is to provide an in-depth understanding of different deep learning paradigms and architectures.

At the end of the course the student should:

- know the key concepts underlying Deep Learning techniques

- be able to apply a wide range of learning algorithms to solve machine learning problems exploiting Deep Learning paradigms

- know how to design and fine-tune deep learning algorithms in order to apply them to new data sets

- be able to evaluate Deep Learning algorithms in order to select the best models

- know python libraries useful for the development of Deep Learning algorithms

Frontal Lessons and Laboratory (in Python)

• Deep Learning - Basic Concepts
• Deep Autoencoders
• Deep Generative Models
• Deep Domain Adaptation
• Deep Metric Learning
• Deep Sequential Modeling
• Deep Reinforcement Learning
• Other Deep Learning Paradigms and State-of-the-Art Architectures

1. Appunti e Slide del Docente
2. E. Alpaydin, Introduction to Machine Learning
3. M. P. Deisenroth et. al, Mathematics for Machine Learning
4. I. Goodfellow et al., Deep Learning
5. Ovidiu Calin, Deep Learning Architectures
6. Sutton and Barto, Reinforcement Learning
7. David Foster, Generative Deep Learning
8. Gabriela Csurka, Domain Adaptation for Visual Applications: A Comprehensive Survey
9. Mei Wang, Weihong Deng, Deep Visual Domain Adaptation: A Survey
10. Aurélien Bellet, Amaury Habrard, Marc Sebban, Metric Learning