2024-2025 / INFO8010-1

Deep learning

Duration

30h Th, 60h Proj.

Number of credits

 Master MSc. in Engineering Physics, research focus5 crédits 
 Master MSc. in Biomedical Engineering, professional focus5 crédits 
 Master MSc. in Computer Science, professional focus in computer systems security5 crédits 
 Master MSc. in Data Science, professional focus5 crédits 
 Master MSc. in Electrical Engineering, professional focus in electronic systems and devices5 crédits 
 Master Msc. in electrical engineering, professional focus in "Smart grids"5 crédits 
 Master MSc. in Data Science and Engineering, professional focus5 crédits 
 Master MSc. in Computer Science and Engineering, professional focus in management5 crédits 
 Master Msc. in computer science and engineering, professional focus in intelligent systems5 crédits 
 Master Msc. in computer science and engineering, professional focus in intelligent systems (double diplômation avec HEC)5 crédits 
 Master MSc. in Computer Science, professional focus in management5 crédits 
 Master Msc. in Energy Engineering, professional focus in Networks5 crédits 
 Master Msc. in Electrical Engineering, professional focus in Neuromorphic Engineering5 crédits 
 Master MSc. in Computer Science and Engineering, professional focus in computer systems and networks5 crédits 
 Master MSc. in Computer Science, professional focus in intelligent systems5 crédits 
 Master MSc. in Computer Science, professional focus in intelligent systems (double diplômation avec HEC)5 crédits 
 Master in geography: geomatics, professional focus in geodata expert5 crédits 
 Master in geography: geomatics, professional focus in land surveyor5 crédits 

Lecturer

Gilles Louppe

Language(s) of instruction

English language

Organisation and examination

Teaching in the second semester

Schedule

Schedule online

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.

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 
The programming project is mandatory to access the exam. 

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)

Association of one or more MOOCs