Intro 2 AI - Fall 2026 class

Note: The course is given in-person (“presentiel”) at CerCo, Pavillon Baudot, Hopital Purpan (Salle de Conference 127, 1st floor).

Schedule (click on each lecture for details)

Topic Lecture # Teacher Sub-topic Date Time
Neural Networks Lecture 1 Jan Bellingrath Neural Networks: History and Foundations Sept 7 1.30-3.30pm
Computer Vision Lecture 2 Bastien Lelan Image classification Sept 7 4-6pm
Computer Vision Lecture 3 Bastien Lelan Unsupervised/zero/few-shot learning Sept 14 1.30-3.30pm
Computer Vision Lecture 4 Jan Bellingrath Object detection, segmentation Sept 14 4-6pm
NLP Lecture 5 Raja Kumar Natural Language Processing basics Sept 21 1.30-3.30pm
NLP Lecture 6 Raja Kumar Recurrent Neural Networks for NLP Sept 21 4-6pm
NLP Lecture 7 Raja Kumar Transformers for NLP & LLMs Sept 28 1.30-3.30pm
Computer Vision Lecture 8 Luca Gonzalez-Sommer Multimodal Learning Sept 28 4-6pm
Audio Lecture 9 Jean-Felix Maestrati Sound processing, speech recognition Oct 5 1.30-3.30pm
DL/Neuro Lecture 10 Rufin VanRullen Homologies between brain & CNNs Oct 5 4-6pm
DL/Neuro Lecture 11a Alexandre Queant Spiking Neural Networks Oct 19 2-3pm
DL/Neuro Lecture 11b Luca Gonzalez-Sommer Brain decoding with Machine Learning Oct 19 3.15-4.15pm

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 Nov 15. Do NOT submit via moodle. This is a firm deadline, the grade will decrease by 1 point for each late day (e.g. if you send your notebook on Nov 25, the maximum grade you can get is 10/20). The main criteria for grading will be: