Intro 2 AI - Spring 2025 class

Note: The course is given in-person (“presentiel”) at CerCo, Pavillon Baudot, Hopital Purpan (Salle de Visio-Conference 129, 1st floor). It will also be broadcast on zoom for students located abroad.

Schedule (click on each lecture for details)

Topic Lecture # Teacher Sub-topic Date Time
Neural Networks Lecture 1 Leopold Maytie Neural Networks: History and Foundations March 4 2-4pm
Computer Vision Lecture 2 Victor Boutin Image classification March 7 2-4pm
Computer Vision Lecture 3 Victor Boutin Unsupervised/zero/few-shot learning March 11 2-4pm
Computer Vision Lecture 4 Mitja Nikolaus Object detection, segmentation March 14 2-4pm
NLP Lecture 5 Romain Bielawski Natural Language Processing basics March 18 2-4pm
NLP Lecture 6 Romain Bielawski Recurrent Neural Networks for NLP March 20 2-4pm
NLP Lecture 7 Romain Bielawski Attention/Transformers in NLP March 25 2-4pm
Computer Vision Lecture 8 Leopold Maytie Multimodal Learning March 28 2-4pm
Audio Lecture 9 Ulysse Rancon Sound processing, speech recognition April 1 2-4pm
TBD Lecture 10 Mathieu Serrurier TBD TBD TBD

Audience

This class, provided by ANITI, is intended for a M2-level audience with no advanced background in Computer Science or Maths (e.g. Neuro, Bio, Medical, etc.). The classes will be in English, and there will be a mixture of lectures and practical work (TP), with at-home assignments. For the practical parts, prior experience with Python programming will be required.

Do you need a refresher on the basics of Python programming before starting our class? To find out, look at [this online course], from lecture “Datatype and Variables” to lecture “Python object and classes” (included). There is nothing to install, all the code examples can be run interactively in the browser. If you already know all the concepts in this course, then your Python level is sufficient. If you’re a beginner, it might take you 10+ hours to go through the course, but this will be necessary to follow our class…

Other useful resources include [this course], or [this one].

Similarly, if you’d like to brush up math concepts that are relevant to modern AI, you could check [this blog] or [this one], or [this two-hour video] or [this one].

Grading

Grading will be based on individual projects. Each student should choose ONE of the course notebooks (from lectures 1-9) and complement it with a significant personal work. Just create one or more cells at the end of the notebook, clearly marked with your personal addition. It could be a change in the model’s architecture, an adaptation of the model to a new task or dataset, etc. If you lack inspiration, feel free to start from the “exercises” or personal work suggested at the end of each notebook (but projects with original and creative thinking will get extra points). The link to the colab notebook (after making sure that it is “shared for anyone with the link”) should be submitted by email to rufin.vanrullen@cnrs.fr on or before May 1st. This is a firm deadline, the grade will decrease by 1 point for each late day (e.g. if you send your notebook on May 11, the maximum grade you can get is 10/20). The main criteria for grading will be: