Duration
30h Th, 60h 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
In an age where sophisticated algorithms drive innovation, deep learning stands at the forefront, underpinning many breakthroughs in science and engineering. From advancing medical diagnostics with image recognition, to reshaping natural language processing, deep learning has become indispensable across many domains.
In this context, this course offers an immersive exploration of deep neural networks, emphasizing end-to-end model development for tasks such as visual recognition, text and speech understanding, or the design of autonomous intelligent systems. Lectures will delve into the details of neural network architectures, ensuring students not only learn the theoretical underpinnings but also master the practical aspects. Students will learn to implement, train and debug their own neural networks and gain a detailed understanding of cutting-edge research in the field.
Topics to be covered (tentative and subject to change):
- Fundamentals of machine learning
- Multi-layer perceptron
- Automatic differentiation
- Training neural networks
- Convolutional neural networks
- Computer vision
- Attention and transformers
- GPT and large language models
- Graph neural networks
- Uncertainty
- Auto-encoders and variational auto-encoders
- Diffusion models
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 practice) and will also have become familiar with some of the many open research questions and challenges of the field.
This course contributes to the learning outcomes I.1, I.2, I.3, II.1, II.2, III.1, III.2, III.3, III.4, IV.1, IV.3, IV.4, V.2, VI.1, VI.2, VII.1, VII.2, VII.4, VII.5 of the MSc in data science and engineering.
This course contributes to the learning outcomes I.1, I.2, II.1, II.2, III.1, III.2, III.3, III.4, IV.1, IV.8, V.2, VI.1, VI.2, VII.1, VII.2, VII.4, VII.5 of the MSc in electrical engineering.
This course contributes to the learning outcomes I.1, I.2, II.1, II.2, III.1, III.2, III.3, III.4, IV.1, V.2, VI.1, VI.2, VII.1, VII.2, VII.4, VII.5 of the MSc in computer science and engineering.
Prerequisite knowledge and skills
Programming experience. Probability and statistics.
Following "ELEN0062 Introduction to Machine learning" before taking this class is strongly recommended.
Planned learning activities and teaching methods
- Theoretical lectures
- Homeworks
- Programming project
Mode of delivery (face to face, distance learning, hybrid 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.
Exam(s) in session
Any session
- In-person
oral exam
Written work / report
Additional information:
Exam(s) in session
Any session
- In-person
oral exam
Written work / report
Additional information:
The evaluation is divided into the following units:
- Exam
- Homeworks
- Programming project
Work placement(s)
Organisational remarks and main changes to the course
The website for the course is https://github.com/glouppe/info8010-deep-learning
Contacts
- Teacher: Prof. Gilles Louppe (g.louppe@uliege.be)