RDFIA - Master DAC - Sorbonne University

This page contains the practical sessions done for the RDFIA course.

The practical sessions are supervised by Nicolas Thome (nicolas.thome[at]sorbonne-universite.fr), Alasdair Newson (anewson[at]isir.upmc.fr), Jayneel Parekh (jayneelparekh[at]gmail.com) and Clement Rambour (rambour[at]isir.upmc.fr).

All coding sessions will be done in Python3 and we'll use several libraries. Here are some useful links if you're rusty:


0: SIFT / Bag of Words / SVM

18 September.
14-15, room 407, 501, and 502.
4PM to 6PM.


Section 1: Basics on deep learning for vision

You need to submit the PDF of 1-ab, 1-cd, and 1-e on Moodle, deadline is Tuesday the 29th of October 2024 at 23:59PM. See homework instructions here.

1-ab: Intro to Neural Networks

25 september & 2 October.
14-15, room 407, 501, and 502.
4PM to 6PM.

1-cd: Convolutional Neural Networks

9 October & 16 October.
14-15, room 407, 501, and 502.
4PM to 6PM.

1-e: Transformers

23 October.
14-15, room 407, 501, and 502.
4PM to 6PM.


Section 2: Deep learning applications

You need to submit the PDF of 2-a, 2-b, 2-c, and 2-de on Moodle, deadline is Tuesday the 24th of December 2024 at 23:59PM. See homework instructions here.

2-a: Transfer Learning

6 November.
14-15, room 407, 501, and 502.
4PM to 6PM.

2-b: Visualization

20 November.
14-15, room 407, 501, and 502.
4PM to 6PM.

2-c: Domain Adaptation

27 November.
14-15, room 407, 501, and 502.
4PM to 6PM.

2-de: Generative Adversarial Networks

4 December & 18 december.
14-15, room 407, 501, and 502.
4PM to 6PM.


Section 3: Bayesian deep learning

You need to submit the PDF of 3-a, 3-b, and 3-c on Moodle, deadline is Tuesday 28 of January 2025 at 23:59PM. See homework instructions here.

3-a: Bayesian linear regression

8 January 2025.
14-15, room 407, 501, and 502.
4PM to 6PM.

3-b: Approximate inference

15 January 2025.
14-15, room 407, 501, and 502.
4PM to 6PM.

3-c: Applications of uncertainty

22 January 2025.
14-15, room 407, 501, and 502.
4PM to 6PM.

Current & Past teacher assistants of this course (or its cousin course at Polytech)