ARTIFICIAL INTELLIGENCE FOR MULTIMEDIA

Academic Year 2022-2023

Teachers

Lauro Snidaro

Unit Credits

Teaching Period

First Period

Course Type

Characterizing

Prerequisites. Requirements: Basic programming skills required. Knowledge of an object-oriented programming language is a plus.

Teaching Methods. The entire course will be held in the laboratory, thus allowing immediate hands-on practice on the concepts acquired.

Verification of Learning. The exam will be based on a project and oral discussion.

The objective of the evaluation will be to assess:

– the knowledge and understanding acquired through questions on the fundamental concepts of Deep Learning and Generative Learning.

– skills in applying acquired knowledge and use of correct terminology through a discussion on the presented project

More Information. ADDITIONAL TEACHING MATERIAL:

Slides, video lessons. All material is available through elearning.uniud.it

THESES:

Theses are available. Contact the instructor.

Objectives

https://www.uniud.it/it/didattica/info-didattiche/regolamento-didattico-del-corso/LM-comunicazione-multimediale-tecnologie-informazione/all-B2

Part I: Fundamentals

Neural networks

Introduction to Deep Learning

Convolutional Networks

Introduction to Generative Models

Variational Autoencoders

Generative Adversarial Networks

Parte II: Applications

Generation of artificial images

Neural Style Transfer

Text generation

Composing music

Texts

Generative Deep Learning: Teaching Machines to Paint, Write, Compose, and Play, O’Reilly, 2019.

Rashid, Tariq. Make your own neural network: a gentle journey through the mathematics of neural networks, and making your own using the Python computer language. CreateSpace Independent Publ., 2016.

Goodfellow, Ian, et al. Deep learning. Vol. 1. Cambridge: MIT press, 2016.

Deep Learning with Keras: Implementing deep learning models and neural networks with the power of Python, Packt, 2017.

Academic Year 2022-2023