Duration
25h Th, 10h Pr, 55h Proj.
Number of credits
Lecturer
Language(s) of instruction
English language
Organisation and examination
Teaching in the second semester
Schedule
Units courses prerequisite and corequisite
Prerequisite or corequisite units are presented within each program
Learning unit contents
Latest developments in deep machine learning have enabled great and unprecedented advances in systems for visual recognition, speech and text understanding or autonomous intelligent agents. In this context, this course is a deep dive into the details of deep learning architectures, with a focus on learning end-to-end models for these tasks. Students will learn to implement, train and debug their own neural networks and gain a detailed understanding of cutting-edge research in the field. The course will also tour recent innovations in inference methods, including differential inference, adversarial training and Bayesian deep learning.
Topics to be covered (tentative and subject to change):
- Fundamentals of machine learning
- Neural networks
- Convolutional neural networks
- Training neural networks
- Recurrent neural networks
- Auto-encoders and generative models
- Generative adversarial networks
- Uncertainty
- Adversarial attacks and defenses
Learning outcomes of the learning unit
At the end of the course, the student will have acquired a solid and detailed understanding of the field of deep learning. He/she will have studied both well-established and novel algorithms (in theory and in practice), and will also have become familiar with some of the many open research questions and challenges of the field.
Prerequisite knowledge and skills
Programming experience. Probability and statistics.
Following "ELEN0062 Introduction to Machine learning" before taking this class is strongly recommend.
Planned learning activities and teaching methods
- Theoretical lectures
- Programming tutorials
- Reading assignment
- Programming project
Mode of delivery (face to face, distance learning, hybrid learning)
Lectures will taught face-to-face. Projects will be carried out remotely.
Organisational adjustments related to the current health context
Recommended or required readings
Slides will be made publicly available on GitHub during the semester.
Assessment methods and criteria
Below you will find information on the evaluation methods planned for in-person and remote exams as well as those planned for hybrid sessions. Depending on how the health crisis evolves, the chosen method will be communicated to you no later than one month before the start of the exam session.
- Exam (50%)
- Reading assignment (10%)
- Programming project (40%)
Work placement(s)
Organizational remarks
The website for the course is https://github.com/glouppe/info8010-deep-learning
Contacts
- Teacher: Prof. Gilles Louppe (g.louppe@uliege.be)
- Assistants: Antoine Wehenkel, Matthia Sabatelli.