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Classes (from 2026–27)

New five-year accreditation

This page describes the new curriculum starting fall 2026 under the new quinquennal accreditation. For the previous program (up to 2025–26), see Classes up to 2025–26.

You may use this guide to select your UE (Unité d'Enseignement), whether you are enrolled in the [AI] track or another track of the Informatics Master's program.

All [AI] courses are open to students from other tracks; however, please ensure that you meet the required prerequisites.

If in doubt, contact the course instructors or the AI Master's program secretary or coordinators.

You can also catch up during the summer by taking one of our online crash courses.

M1 Classes

T1 — First period

Applied Statistics

This course introduces the necessary formalism to solve computational problems with statistical reasoning. The goal is to provide students with the necessary skills to employ statistical modeling in a computational framework to tackle real world problems.

Prior knowledge: basic linear algebra.

Acquired skills:

  1. Give a statistical description of a dataset.
  2. Leverage probabilistic modeling to perform statistical inference.
  3. Analyze the mathematical properties of a statistical estimator.
  4. Employ bootstrapping.
  5. Perform hypothesis testing.

References: All of Statistics: A Concise Course in Statistical Inference — Larry Wasserman (PDF)

Hands-on Machine Learning with Scikit-learn

A practical oriented class, where students apply ML techniques to simple illustrative examples and then to tackle competitive challenges. It will start with an introduction to present (refresh) the ML landscape. Classes will then be articulated to successively focus on the major concepts of practical ML.

Outline:

  1. Introduction/refresher on ML
  2. Working with real data
  3. Discover and visualize the data to gain insights
  4. Prepare the data for processing
  5. Select and train models
  6. Fine-tune models

Recommended reading:

  • Géron (2019) Hands-on Machine Learning with Scikit-Learn, Keras, and Tensorflow
  • VanderPlas (2017) Python Data Science Handbook
Mathematics for Data Science — class from other track

This class aims at teaching/reminding mathematical basis useful in data science:

  1. Vector spaces, linear transformations
  2. Matrices, linear systems
  3. Trace, determinant
  4. Norms, orthogonality
  5. Eigenvalues, singular value decomposition
  6. Tensors (notions), multivariable calculus

The course is evaluated by a written exam.

References:

Advanced Databases — class from other track

This course aims at enabling the students to learn the fundamentals of Relational Database Management Systems (DBMS) and apply these concepts in practice.

  1. Basics of relational databases
  2. Design theory for relational databases
  3. Relational algebra and SQL
  4. High-level database models

Recommended reading:

  • Database Systems: The Complete Book — Garcia-Molina, Ullman, Widom (2nd ed.)
  • Database Management Systems — Ramakrishnan & Gehrke, McGraw-Hill
  • A First Course in Database Systems — Ullman & Widom
Exploring Data Declaratively: Constraints and Patterns — class from other track

T2 — Second period

Machine Learning Basics 1

This course is algorithms-oriented: we sketch the great principles of ML at first, and then focus on how algorithms work in practice, including all necessary mathematical aspects. They are the basic building blocks of more advanced algorithms.

  1. Gradient Descent, Linear Regression from scratch
  2. Classification with a single layer Perceptron, from scratch. Geometrical interpretation, SGD/mini-batch learning. Discussion on the choice of the loss function or activation functions. OVR multi-class scheme.
  3. Overfitting, train/validation/test split, K-fold CV, regularization (L2, L1)
  4. MAP, Bayesian interpretation of Ridge Regression or Lasso
  5. Feature maps ("Kernel trick"), PCA (from scratch, seen as variance maximization), PCA as pre-processing (dimensional reduction)
  6. Kernels, Kernelized perceptron, SVM in the separable case

Acquired skills:

  • Know the basics of ML vocabulary
  • Make good habits, understand the standard pipeline
  • Know standard supervised, shallow-ML algorithms (be able to write their pseudo-code, explain their functioning)
  • Be able to code an algorithm by reading its documentation

Recommended reading:

  • Bishop (2006) Pattern Recognition and Machine Learning (available online for free)
  • Géron Hands-on Machine Learning with Scikit-Learn and TensorFlow — also in French: Introduction au Machine Learning
Optimization

This Optimization for Machine Learning course aims at studying the mathematical and computational constructions and properties of key optimization algorithms in different situations (continuous/discrete, constrained/unconstrained, convex/non-convex, deterministic, and stochastic problems) with use case illustrations in machine learning.

  1. Introductions and background (convexity, differentiability, optimality conditions, convergence rates…)
  2. Continuous optimization (first order methods: gradient methods, linear search, acceleration)
  3. Continuous optimization (second order methods: Newton methods including Quasi-Newton, secant, IRLS)
  4. Constrained optimization (equality and inequality constraints, duality/Lagrangian, KKT optimality conditions, linear programming, GD for a constrained problem)
  5. Non-convex, stochastic optimization (the EM algorithm, stochastic gradient, stochastic EM)

Readings:

  • Convex Optimization — Boyd and Vandenberghe
  • Numerical Optimization — Nocedal and Wright
  • Optimization — Kenneth Lange

Course page

Datacamp

This course aims to learn the practical tools for data science and how to frame and solve data science problems.

  1. Data wrangling
  2. The scikit-learn API and missing values
  3. Metrics and unbalanced data
  4. Dealing with complex data
  5. Ensemble methods and hyperparameter optimization

Full syllabus on GitHub

T3 — Third period

Machine Learning Basics 2

This course aims at mastering the core concept of algorithmic design in ML, from an optimization or a probabilistic point-of-view, using supervised and unsupervised algorithms.

  1. Regression/classification seen in optimization and probabilistic frameworks, implication on batch and stochastic gradient descent
  2. Learning theory and Vapnick-Chervonenkis dimension
  3. Evaluating performances of ML algorithms in different contexts (imbalanced, small-sized, etc.)
  4. Probabilistic framework for machine learning: Discriminative vs Generative learning, Empirical Risk Minimization, Risk Decomposition, Bias-Variance Tradeoff; MLE, MLE and OLS in regression, MLE and IRLS in softmax classification
  5. Unsupervised Learning and Clustering: K-means, Mixture Models, EM algorithms
  6. Unsupervised Learning and Dimensionality reduction: PCA, Probabilistic PCA & EM, ICA

Recommended reading:

  • James, Witten, Hastie & Tibshirani (2013) An Introduction to Statistical Learning
  • Hart, Stork & Duda (2000) Pattern Classification
  • Cornuéjols & Miclet (2011) Apprentissage artificiel: concepts et algorithmes
Hands-on Natural Language Processing (2 groups)
  1. Introduction to NLP
  2. Basic concepts in NLP, tokenization, lemmatisation, POS tagging, ...
  3. Lexical semantics, word sense disambiguation
  4. Syntax and interpretations
  5. Parsing

Recommended reading: Natural Language Processing with Python

Foundations of Agent-based Systems — class from other track

T4 — Fourth period

Deep Learning

The aim of this course is to introduce the Deep-Learning framework. It will cover fundamental models such as the multilayer perceptron through to the most recent deep learning architectures (CNN, VAE, ...). In addition, the course will cover the different approaches to train these neural networks, with lectures dedicated to backpropagation algorithms and optimization methods based on gradient descent.

  1. Introduction to Neural Networks and the MLP model
  2. MLP and Gradient Descent algorithm
  3. Backpropagation algorithm and optimization methods
  4. Create your neural network with PyTorch
  5. Neural Networks architectures (CNN, AE, ...)
  6. Generative approaches (VAE, GAN, Denoising Diffusion models)

Acquired skills:

  • Broad view of NN architectures
  • Training and evaluating NN
  • Use of PyTorch
  • Implementing backpropagation algorithm

Recommended reading: Deep Learning Book

Speech and Language Processing

Various talks on the following subjects:

  1. From linguistics to NLP
  2. Treebanks and oral syntax
  3. NLP, semantics, multi-word expressions
  4. Speech-Audio processing + ASR + practical work
  5. Emotion detection
  6. Speech interaction

Keywords: ongoing NLP research at LISN

Creation of a Data Challenge

This course is designed to bridge theory (lectures) and practice (TPs) by guiding students through the end-to-end creation and resolution of an AI challenge, using pre-formatted real datasets and the open-source platform Codabench.

  1. Designing AI Challenges
  2. Evaluation and Metrics
  3. Baselines: Core ML algorithms
  4. Baselines: Image Classification
  5. Optimizing ML Solutions
  6. Interpreting Results & Presenting Impact
  7. Project presentations

Full syllabus

Deliverables:

  • A Codabench challenge (including: website, GitHub repo, starting kit with a Python notebook)
  • A challenge/benchmark leaderboard with submissions from classmates
  • A written report (with conference publication potential if high quality)
  • An oral project presentation

Projects, internships, and other M1 modules

  • TER et/ou Stage et/ou Écoles (12 ECTS):
    • TER (3 ECTS) + Stage 3 mois (9 ECTS)
    • TER (6 ECTS) + Stage 2 mois (6 ECTS)
    • Stage 4 mois (12 ECTS)
  • UE libre (3 ECTS)
  • UE Sustainable Development (3 ECTS)
  • 3 UE from other tracks (3 × 3 = 9 ECTS): 2 mandatory + 1 elective

For more details on internships and TER, see M1 Internships & TER.

M2 Classes (8 UE propres = 24 ECTS)

T5 — Fifth period

Frugal AI

14 hours of lectures and evaluation + 7 hours of lab work.

Artificial intelligence algorithms are often built under the assumption that vast amounts of (labeled) data are available and that computational resources and memory are not constrained. These assumptions do not hold in a large number of practical applications, and this course will address situations in which constraints apply.

  1. Discussion of different types of frugality (data, model parameters, training/inference constraints, memory), illustration with practical examples, and the relation between frugal learning and large models
  2. Label frugality: supervised, weakly-supervised, semi-supervised, and self-supervised learning
  3. Data frugality: transfer, few-shot, zero-shot, and continual learning
  4. Frugal vs. large deep models and applications

The three labs will focus on self-supervised, few-shot, and continual learning, respectively.

Prerequisites: at least basic knowledge of ML and preferably at least one introductory course in deep learning.

Keywords: pretraining, large models, transfer learning, data scarcity

Deep Learning for NLP
  1. Introduction, language models
  2. Convolutional neural nets
  3. Recurrent neural networks
  4. Attention mechanisms
  5. Word representations, Transformers
  6. Generative models

Keywords: machine learning, statistics, probability theory, Python

NLP Today
  1. Introduction (Cyril Grouin)
  2. Text Mining in Open and Medical Domain (Aurélie Névéol)
  3. Text Mining in Open and Medical Domain (Aurélie Névéol)
  4. Semantics and Word Embeddings (Sahar Ghannay)
  5. Chatbots and Evaluation (Thomas Gerald)
  6. Chatbots and Evaluation (Thomas Gerald)

Keywords: text processing, word representations, neural networks for texts, dialogue systems

Course page

From Symbolic to Neurosymbolic AI — class from other track

T6 — Sixth period

Reinforcement Learning
  1. Introduction to Reinforcement Learning
  2. Markov Decision Processes
  3. Planning by Dynamic Programming
  4. Model-Free Prediction
  5. Model-Free Control
  6. Value Function Approximation
  7. Policy Gradient Methods

Keywords: machine learning, statistics, probability theory, Python

Information Retrieval

This course gives a basic introduction to Information Retrieval.

  1. Introduction to Information Retrieval: key terms and domains; tutorial on new textual dataset indexing and basic counting techniques
  2. Handling Large Datasets: exploration of big datasets; binary evaluation methods; introduction to TF-IDF
  3. Improving Retrieval Methods: introduction to sparse embeddings; overview of BM25 and Sense2Vec
  4. Advanced Embedding Techniques: study of dense embeddings; use case with a patent dataset and its citations; introduction to doc2vec and sentenceBERT
  5. Challenge Presentation: class challenge related to information retrieval concepts
  6. Project Work and Presentation: collaborative project work and discussions; final project presentations

Recommended reading:

  • Manning et al. Introduction to Information Retrieval
  • Grainger et al. AI Powered Search

Keywords: Hands-On Knowledge of the Foundations of AI-Powered Search

Scientific Machine Learning

ML is increasingly adopted as a useful tool in the exact sciences (Physics, Chemistry, etc), where abundant precise data are available or can be generated with simulators. Applications include drug design, material design, genetics, applied quantum mechanics, fluid mechanics, and many others.

  1. Introduction to Scientific Machine Learning — applications in Physics, Chemistry, and Biology
  2. Graph Neural Networks and Geometric Deep Learning
  3. NeuralODEs and PINNs
  4. Steerable and Equivariant Neural Networks
  5. Attention Mechanisms across Architectures (GAT, EquiFormer, ViT)

This course introduces paradigms relevant to Scientific ML such as GNNs, NeuralODEs, PINNs, and the general framework of Geometric Deep Learning, including steerable neural networks (e.g. rotation-equivariant nets). It also dives deeper into the attention mechanism (e.g. for Vision Transformers), since the attention block is now of widespread use across other architectures.

Keywords: Graph Neural Networks (GNNs), NeuralODEs, Physics-Informed Neural Networks (PINNs), Geometric Deep Learning, Transformers

T7 — Seventh period

Signal Processing
  1. Introduction & Fourier analysis
  2. Filtering
  3. Random signals
  4. Time-Frequency analysis
  5. Time-Scale analysis
  6. Introduction to linear inverse problems

Keywords: spectral analysis of time signals and images, convolution and filtering, time-frequency and wavelets analysis, denoising, sparse coding for inverse problem resolution

Course page

Advanced Optimization and Automated Machine Learning
  1. Introduction to Optimization
  2. Unconstrained Continuous Optimization
  3. Constraint Optimization
  4. Black Box Optimization
  5. Hyper-parameter Optimization
  6. Neural Architecture Search + Learning to Optimize & Meta-Learning

Keywords: optimization, black-box optimization, Bayesian optimization, neural architecture search, hyperparameter optimization, meta-learning, AutoML

Probabilistic Generative Models

This module covers probabilistic and neural generative models, from theoretical foundations to modern applications in vision, language, and multimodal AI.

  1. Foundations of Generative Models — definitions, probability basics, explicit vs. implicit models, and first applications
  2. Graphical Models & HMMs — Bayesian and Markov networks, exact/approximate inference, and sequential models
  3. Neural Generative Models (Images) — variational autoencoders (VAEs), GANs, and diffusion models for image synthesis
  4. Text Generation with Transformers — Transformer architecture, attention, pre-training (BERT, GPT)
  5. Optimization & Advanced LLM — efficient adaptation of large models with techniques like LoRA
  6. Emerging Models — Liquid Neural Nets, HRM, state-space & quantum-inspired approaches

Keywords: notions of statistics and AI

Soft skills & internship

  • Soft Skills (6 ECTS) — includes Trust and professional development
  • Stage (30 ECTS) — 5 to 6 month internship in a research lab or a company