Duration
30h Th, 5h Pr, 45h 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
- Multi-layer perceptron
- Convolutional neural networks
- Training neural networks
- Recurrent neural networks
- Unsupervised learning
- Differentiable inference and generative models
- Theory of deep learning
- Adversarial training
- Bayeisan deep learning
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
- Reading assignment
- Programming projects
Mode of delivery (face-to-face ; distance-learning)
Lectures will taught face-to-face. Projects will be carried out remotely.
Recommended or required readings
Slides will be made publicly available on GitHub during the semester.
Assessment methods and criteria
- Exam (50%)
- Reading assignment (10%)
- Programming projects (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: TBD.