Lectures for INFO8010 Deep Learning, ULiège, Spring 2026.
- Instructor: Gilles Louppe
- Teaching assistants: Fanny Bodart, Elise Faulx, Julien Brandoit, Sacha Peters
- When: Spring 2026, Friday 8:30 AM
- Classroom: B28 / Mania Pavella amphitheater
- Discord: https://discord.gg/5yZqTZhXFW
| Date | Topic |
|---|---|
| February 6 | Course syllabus [PDF] Lecture 0: Introduction [PDF] Lecture 1: Fundamentals of machine learning [PDF] |
| February 13 | Lecture 2: Multi-layer perceptron [PDF] [code 1, code 2] |
| February 20 | Lecture 3: Automatic differentiation [PDF] [code] |
| February 27 | Lecture 4: Training neural networks [PDF] |
| March 6 | Lecture 5: Convolutional neural networks [PDF] [code] |
| March 13 | Lecture 6: Computer vision [PDF] [code] |
| March 20 | Lecture 7: Attention and transformers [PDF] |
| March 27 | Code: GPT, from scratch! Lecture 8: LLMs and foundation models [PDF] |
| April 3 | Lecture 9: Graph neural networks [PDF] |
| April 10 | Lecture 10: Uncertainty [PDF] |
| April 17 | Lecture 11: Auto-encoders and variational auto-encoders [PDF] [code] |
| May 8 | Lecture 12: Diffusion models [PDF] |
The goal of these two assignments is to get you familiar with the PyTorch library. You can find the installation instructions in the Homeworks folder. Each homework should be done in groups of 2 or 3 (the same as for the project) and must be submitted before 23:59 on the due date. Homeworks should be submitted on Gradescope.
- Homework 1: Tensor operations,
autogradandnn. Due by (TBD). - Homework 2: Dataset, Dataloader, running on GPU, training a convolutional neural network. Due by (TBD).
Homeworks are optional. If submitted, each homework will account for 5% of the final grade.
See instructions in project.md.
Due to progress in the field, some of the lectures have become less relevant. However, they are still available for those who are interested.
| Topic |
|---|
| Recurrent neural networks [PDF] [video] |
| Generative adversarial networks [PDF] [video] |