M18 - Artificial Neural Networks: from the Ground Up

Type of Course - Dates - Venue - Description - Target audience - Exam - IMPORTANT: Incorporation in DTP and reimbursement by DS
Course prerequisites - Teacher - Course material - Fees - Enrol

Type of course

 This is an on campus course, with blended learning options.

Dates

Five Thursday evenings in April and May 2022: April 21 and 28, May 5, 12 and 19, 2022, from 5.30 pm to 9 pm
Please note: The deadline for UGent PhD students who want a refund to open a dossier on the DS website (Application for Recognition) is March 21, 2022.

Venue

 To be confirmed

Description

Since their earliest conception in the 1940s, artificial neural networks have been alternatively regarded as extremely promising machine learning models, capable of learning anything, and as glorified linear combinations, unable to achieve relevant results in practice.

However, along the last decade, the availability of general-purpose GPU architectures and large quantities of data has enabled the rise of deep neural networks, which have attained state-of-the-art performance in many applications, from image classification to text translation. This has given rise to a whole new field of research, ranging from generative models to adversarial attacks (and defenses against them).

This course is intended as a first contact with artificial neural networks, followed by an overview of the different architectures that are currently available:

  • Introduction to neurons and neural networks
  • Training with backpropagation
  • Challenges and solutions to train deep neural networks
  • Convolutional networks
  • Adversarial examples
  • Generative models
    • Autoregressive models
    • Autoencoders
    • Variational autoencoders (VAE)
    • Generative adversarial networks (GAN)
  • Transformers and BERT
  • Recurrent neural networks

The practical sessions use the Python library TensorFlow to implement some of the models discussed in the course, with particular emphasis on how to adapt the networks to the characteristics of a specific problem.

Target audience

This course is aimed at professionals and investigators from diverse areas who want to learn how to apply neural networks on diverse problems, or who want to learn about the possibilities, applicability, and variants of neural networks.

Exam

Participants can, if they wish, take part in an exam. Upon succeeding in this test a certificate from Ghent University will be issued.
The exam consists of a take home project assignment. Students are required to write a report by a set deadline.

Incorporation in DTP and reimbursement from DS for UGent PhD students

As a UGent PhD student, to be able to incorporate this course in your Doctoral Training Program (DTP) and get a reimbursement of the registration fee from your Doctoral School (DS) you need to follow strict rules: please take the necessary action in time. The deadline to open a dossier on the DS website (Application for Recognition) for this course is March 21, 2021. Please note that opening a dossier does not mean that you are enrolled. You still need to enrol via the registration form on this site.

Please note: For UGent PhD students it is no longer necessary to participate/succeed in this exam to be able to incorporate the course in the DTP.

Course prerequisites

Basic knowledge of the Python programming language is required.

Teacher

Foto Daniel PeraltaDr. Daniel Peralta is a post-doctoral researcher at the Department of Applied Mathematics, Computer Science and Statistics of the Faculty of Sciences of Ghent University. He obtained his PhD at the University of Granada (Spain), tackling large-scale fingerprint identification.
His research has focused on machine learning, especially in large-scale scenarios, and has involved several collaborations with industry to apply such techniques on problems ranging from railway maintenance scheduling to compound activity prediction. Within his current position at the VIB, this research is applied on biological data. He currently teaches Big Data Science courses at Ghent University, in the Master of Statistical Data Analysis and the Master in Computer Science.

Course material

Slides and code for the practical sessions

Fees

A different price applies, depending on your main type of employment.

Employment Module 18 Exam
Industry/Private sector1 925 30
Non-profit, government, higher education staff2 695 30
(Doctoral) students, retired, unemployed2 310 30

1 If two or more employees from the same company enrol simultaneously for this course a reduction of 20% on the module price is taken into account, starting from the second enrolment.

2 UGent-staff and UGent doctoral students who pay internally via SAP or internal transfer can participate at these special rates.

Enrol for this course