Target

Duration

20 hours

Instructors

Nicoletta Noceti
UniGe | MaLGa & DIBRIS nicoletta.noceti@unige.it

When

Jun 28 2021 , Jul 2 2021

Where

Live streaming platform TBA hopefully relaxing covid restrictions will let the course to be also delivered in-person: Via Dodecaneso 35, Genova, Italy

Important Dates

Application deadline: May 16
Notifications of acceptance have been sent out
The school will be held online on Microsoft Teams
We sent the invitations to join the DL team on Microsoft Teams. If you have not received the invitation, please reach out at malga.unige@gmail.com

Abstract

Deep Learning is a branch of Machine Learning that has recently achieved astonishing results in a number of different domains.
This course will provide a hands-on introduction to Deep Learning, starting from its foundations and discussing the various types of deep architectures and tools currently available.
The theoretical classes will be accompanied by work in lab, which will constitute an integral part of the course, giving the possibility of practicing deep learning with examples from real-world applications, with particular focus on visual data.
Besides well established approaches, the course will also highlight current trends, open problems and potential future lines of research.

A certificate of attendance (2 credits suggested according to the ECTS grading scale) will be sent to all participants.

An exam certificate (no grade, 6 credits suggested according to the ECTS grading scale) will be issued to those who will take and pass the exam

The course is linked to the Computer Vision Crash Course. The two courses are self-contained and can be taken independently, but students interested in both will be provided extra information to better relate the two subjects.

Mon 9.00-10:30 Class Intro to Deep Learning: from single layer perceptron to Deep Neural Networks
Mon 10.45-12.15 Lab I Training a (deep) neural network
Mon 12:30-13:00 Interlude Team project design introduction (part I)
Tue 9.00-11.00 Class -Convolutional Neural Networks
Tue 11.15-13.00 Lab II Image classification with CNNs
Wed 9.00-10.15 Class Dealing with sequences: Recurrent Neural Networks
Wed 10:30-11.45 Lab III Text classification with RNNs
Wed 12:00-13:00 Seminar Elisa Ricci (Uni. of Trento, FBK)
Thu 9.00-10.15 Class Autoencoders&GANs
Thu 10.30-12.00 Lab IV GANs on images
Thu 12:00-13:00 Interlude Team project design introduction (part II)
Fri 9:00-11:00 Team project design
Fri 11:00-13:00 Team project presentation and closing

Elisa Ricci

Associate Professor with Department of Information Engineering and Computer Science (DISI) at the University of Trento and head of the Deep Visual Learning research group at Fondazione Bruno Kessler (FBK).

Once accepted, each candidate has to follow the instructions in the acceptance email and proceed with the payment. The registration fee is non-refundable.

Fees
students and postdocs: waived
professors: waived
professionals: EUR 150
UniGe students and IIT affiliates: no fee

To apply, complete the application form

Vito Paolo Pastore
UniGe | MaLGa & DIBRIS Vito.Paolo.Pastore@edu.unige.it
Modiana Pasquinelli UniGe | MaLGa & DIBRIS modiana@unige.it

Goodfellow, Y. Bengio and A. Courville, Deep Learning book, MIT Press, 2016
Francois Chollet. Deep Learning with Python, Manning Pub., 2017.

Slides, notebooks, and a list of bibliographical references and additional material will be provided to attendants. All the course material is in English.