OVERVIEW

The course will provide an overview of principles behind neural networks and deep architectures,covering classical and recent approaches

AIMS AND CONTENT

LEARNING OUTCOMES

Learning how to use deep learning algorithms, including classical approaches and very recent networks, by grasping the underlying computational and modeling issues.

AIMS AND LEARNING OUTCOMES

Students will be provided with an overview of neural networks and deep architectures, starting from basic principles to more advanced concepts. An overview of the different types of architecture will be presented. Also, recent methodologies will be introduced to discuss practical problems related for instance to computational aspects, data requirements, and generalization abilities.
Hands-on activities, in which students will practice the use of neural networks, will always complement the theoretical classes.  The students will deepen their capability of critically analysing the results.

At the end of the course, students will be able to:

UNDERSTAND and use classical and more recent deep learning methodologies

UNDERSTAND how to effectively set-up deep learning pipelines

IMPLEMENT deep learning approaches presented in the course

IMPROVE the ability to critically analyze analytical results

PREREQUISITES

Basic of Machine Learning, programming (preferrable in Python)

TEACHING METHODS

Theoretical classes will be coupled with practical lab sessions

Students will be asked to work in groups during such lab sessions.

The last part of the course will be devoted to the development of a project

SYLLABUS/CONTENT

The course will cover the following topics:

• Neural Networks
• Convolutional Neural Networks
• Recurrent Neural Networks
• LSTMs
• Transformers
• Graph Neural Networks
• Autoencoders and GANs
• Deep clustering
• Representation Learning strategies
• Transfer Learning and domain adaptation

RECOMMENDED READING/BIBLIOGRAPHY

The material will provided by the instructors on the Aulaweb page of the course.

TEACHERS AND EXAM BOARD

Exam Board

NICOLETTA NOCETI (President)

VITO PAOLO PASTORE

FRANCESCA ODONE (Substitute)

LESSONS

LESSONS START

In agreement with the academic calendar approved by the Committee of the Study Courses in Informatics and Computer Science

EXAMS

EXAM DESCRIPTION

The exam will consist in two main parts:

• a project that will include the submission of material produced by the students, and an oral examination (no project if number of credits < 9)
• an oral exam

ASSESSMENT METHODS

The exam will evaluate the overall understanding of the topics of the course, the capability to generalize the concepts to unseen problems and analyse the obtained results.
Clarity of exposition, completeness of the concepts, quality of the proposed solutions and critical thinking will be taken into account.

Exam schedule