glouppe / Info8010 Deep Learning
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INFO8010 - Deep Learning
Lectures for INFO8010 - Deep Learning, ULiège, Spring 2021.
- Instructor: Gilles Louppe
- Teaching assistants: Matthia Sabatelli, Antoine Wehenkel
- When: Spring 2021, Friday 9:00 AM
- Classroom: Lectures are virtual and broadcast on Youtube.
- Discussion: Join us on Discord (see invitation link in emails)
Agenda
| Date | Topic |
|---|---|
| February 5 |
Course syllabus [PDF] [video] Lecture 0: Introduction [PDF] [video] Lecture 1: Fundamentals of machine learning [PDF] [Side notes] [video] |
| February 12 | Lecture 2: Multi-layer perceptron [PDF] [Side notes] [video] |
| February 19 | Lecture 3: Automatic differentiation [PDF] [Side notes] [video] |
| February 26 | Lecture 4: Training neural networks [PDF] [Side notes] [video] |
| March 1 | Deadline for Homework 1 |
| March 5 | Lecture 5: Convolutional neural networks [PDF] [Side notes] [video] |
| March 12 | Lecture 6: Computer vision [PDF] [Side notes] [video] |
| March 19 Live stream |
Lecture 7: Recurrent neural networks [PDF] |
| March 22 | Deadline for Homework 2 |
| March 26 | Lecture 8: Attention and transformer networks Deadline for the project proposal |
| April 2 | Lecture 9: Generative models (part 1) |
| April 23 | Lecture 10: Generative models (part 2) |
| April 30 | Lecture 11: Uncertainty Deadline for the reading assignment |
| May 7 | Lecture 12: Deep reinforcement learning |
| May 14 | Deadline for the project (code and report) |
Homeworks
The goal of these two assignments is to get you familiar with the PyTorch library. Each homework should be done in groups of 2 or 3 (the same as for the project) and must be submitted on the Montefiore submission platform before 23:59 on the due date.
-
Homework 1: Tensor operations,
autogradandnn. Due by March 1. - Homework 2: Dataset, Dataloader, running on GPU, training a convolutional neural network. Due by March 22.
Homeworks are optional. If submitted, each homework will account for 5% of the final grade.
Project
See instructions in project.md.
Reading assignment
Your task is to read and summarize a major scientific paper in the field of deep learning. You are free to select one among the following three papers:
You should produce a report that summarizes the problem that is tackled by the paper and explains why it is challenging or important, from the perspective of the wider research context. The report should outline the main contributions and results with respect to the problem that is addressed. It should also include a critical discussion of the advantages and shortcomings of the contributions of the paper. Further guidelines for writing a good summary can be found here (Section 2, "The summary").
Constraints:
- You can work in groups of maximum 3 students.
- You report must be written in English.
- 2 pages (excluding references, if any).
- Formatted using the LaTeX template
template-report.tex.
Your report should be submitted by April 30, 2021 at 23:59 on the submission platform. This is a hard deadline.