Descriptif des cours
Cours obligatoires
- Optimization for Machine Learning
Optimization for Machine Learning
Ects : 4
Enseignant responsable :
Volume horaire : 24
Description du contenu de l'enseignement :
Optimization has long been a fundamental component for modeling and solving classical machine learning problems such as linear regression and SVM classification. It also plays a key role in the training of neural networks, thanks to the development of efficient numerical tools tailored to deep learning. This course is concerned with developing optimization algorithms for learning tasks, and will consist of both lectures and hands-on sessions in Python. The course will begin by an introduction to the various problem formulations arising in machine and deep learning, together with a refresher on key mathematical concepts (linear algebra, convexity, smoothness). The course will then describe the main algorithms for optimization in data science (gradient descent, stochastic gradient) and their theoretical properties. Finally, the course will focus on the challenges posed by implementing these methods in a deep learning and large-scale environment (automatic differentiation, distributed calculations, regularization).
Compétences à acquérir :
- Understand the nature and structure of optimization problems arising in machine learning.
- Select an algorithm tailored to solving a particular instance among those seen in class based on theoretical and practical concerns.
- Experience the practical challenges in implementing an optimization scheme in a learning setting.
Bibliographie-lectures recommandées
- L. Bottou, F. E. Curtis and J. Nocedal. Optimization Methods for Large-Scale Machine Learning. SIAM Review, 2018.
- S. J. Wright and B. Recht. Optimization for Data Analysis. Cambridge University Press, 2022.
- Foundations of Machine Learning
Foundations of Machine Learning
Ects : 4
Enseignant responsable :
- FRANCIS BACH
Volume horaire : 24
Description du contenu de l'enseignement :
The course will introduce the theoretical foundations of machine learning, review the most successful algorithms with their theoretical guarantees, and discuss their application in real world problems. The covered topics are:
-
Part 1: Supervised Learning Theory: the batch setting
- Intro
- Surrogate Losses
- Uniform Convergence and PAC Learning
- Empirical Risk Minimization and ill-posed problems
- Concentration Inequalities
- Universal consistency, PAC Learnability
- VC dimension
- Rademacher complexity
- Non Uniform Learning and Model Selection
- biais-variance tradeoff
- Structural Minimization Principle and Minimum Description Length Principle
- Regularization
-
Part 2: Supervised Learning Theory and Algorithms in the Online Setting
- Foundations of Online Learning
- Beyond the Perceptron algorithm
-
Partie 3: Ensemble Methods and Kernels Methods
- SVMs, Kernels
- Kernel approximation algorithms in the primal
- Ensemble methods: bagging, boosting, gradient boosting, random forests
-
Partie 4: Algorithms for Unsupervised Learning
- Dimensionality reduction: PCA, ICA, Kernel PCA, ISOMAP, LLE
- Representation Learning
- Expectation Maximization, Latent models and Variational methods
Pré-requis recommandés :
- Linear models
Pré-requis obligatoire :
- Linear Algebra - Statistics and Probability
Compétences à acquérir :
The aim of this course is to provide the students with the fundamental concepts and tools for developing and analyzing machine learning algorithms.
Mode de contrôle des connaissances :
- Each student will have to have the role of scribe during one lecture, taking notes during the class and sending the notes to the teacher in pdf. - Final exam
Bibliographie-lectures recommandées
The most important book: - Shalev-Shwartz, S., & Ben-David, S. (2014). Understanding machine learning: From theory to algorithms. Cambridge university press. Also: - Mohri, M., Rostamizadeh, A., & Talwalkar, A. (2012). Foundations of machine learning. MIT press. - Vapnik, V. (2013). The nature of statistical learning theory. Springer science & business media. - Bishop Ch. (2006). Pattern recognition and machine learning. Springer - Friedman, J., Hastie, T., & Tibshirani, R. (2001). The elements of statistical learning (Vol. 1, No. 10). New York, NY, USA:: Springer series in statistics. - James, G., Witten, D., Hastie, T., & Tibshirani, R. (2013). An introduction to statistical learning (Vol. 112). New York: springer.
- Reinforcement learning
Reinforcement learning
Ects : 4
Enseignant responsable :
- OLIVIER CAPPE
Volume horaire : 24
Description du contenu de l'enseignement :
- Models: Markov decision processes (MDP), multiarmed bandits and other models
- Planning: finite and infinite horizon problems, the value function, Bellman equations, dynamic programming, value and policy iteration
- Basic learning tools: Monte Carlo methods, temporal-difference learning, policy gradient
- Probabilistic and statistical tools for RL: Bayesian approach, relative entropy and hypothesis testing, concentration inequalities
- Optimal exploration in multiarmed bandits: the explore vs exploit tradeoff, lower bounds, the UCB algorithm, Thompson sampling
- Extensions: Contextual bandits, optimal exploration for MDP
Compétences à acquérir :
Reinforcement Learning (RL) refers to scenarios where the learning algorithm operates in closed-loop, simultaneously using past data to adjust its decisions and taking actions that will influence future observations. Algorithms based on RL concepts are now commonly used in programmatic marketing on the web, robotics or in computer game playing. All models for RL share a common concern that in order to attain one's long-term optimality goals, it is necessary to reach a proper balance between exploration (discovery of yet uncertain behaviors) and exploitation (focusing on the actions that have produced the most relevant results so far).
The methods used in RL draw ideas from control, statistics and machine learning. This introductory course will provide the main methodological building blocks of RL, focussing on probabilistic methods in the case where both the set of possible actions and the state space of the system are finite. Some basic notions in probability theory are required to follow the course. The course will imply some work on simple implementations of the algorithms, assuming familiarity with Python.
Mode de contrôle des connaissances :
- Individual homework (in Python)
- Final exam
Bibliographie-lectures recommandées
Bibliographie, lectures recommandées
- M. Puterman. Markov Decision Processes: Discrete Stochastic Dynamic Programming. John Wiley & Sons, 1994.
- R. Sutton and A. Barto. Introduction to Reinforcement Learning. MIT Press, 1998.
- C. Szepesvari. Algorithms for Reinforcement Learning. Morgan & Claypool Publishers, 2010.
- T. Lattimore and C. Szepesvari. Bandit Algorithms. Cambridge University Press. 2019.
- Data Science Lab
Data Science Lab
Ects : 4
Enseignant responsable :
Volume horaire : 24
Description du contenu de l'enseignement :
Students enrolled in this class will form groups and choose one topic among a list of proposed topics in the core areas of the master such as supervised or unsupervised learning, recommendation, game AI, distributed or parallel data-science, etc. The topics will generally consist in applying a well-established technique on a novel data-science challenge or in applying recent research results on a classical data-science challenge. Either way, each topic will come with its own novel scientific challenge to address. At the end of the module, the students will give an oral presentation to demonstrate their methodology and their findings. Strong scientific rigor as well as very good engineering and communication skills will be necessary to complete this module successfully.
Compétences à acquérir :
The goal of this module is to provide students with a hands-on experience on a novel data-science/AI challenge using state-of-the-art tools and techniques discussed during other classes of this master.
- Large language models
Large language models
Ects : 4
Enseignant responsable :
Volume horaire : 24
Description du contenu de l'enseignement :
The course focuses on modern and statistical approaches to NLP.
Natural language processing (NLP) is today present in some many applications because people communicate most everything in language : post on social media, web search, advertisement, emails and SMS, customer service exchange, language translation, etc. While NLP heavily relies on machine learning approaches and the use of large corpora, the peculiarities and diversity of language data imply dedicated models to efficiently process linguistic information and the underlying computational properties of natural languages.
Moreover, NLP is a fast evolving domain, in which cutting-edge research can nowadays be introduced in large scale applications in a couple of years.
The course focuses on modern and statistical approaches to NLP: using large corpora, statistical models for acquisition, disambiguation, parsing, understanding and translation. An important part will be dedicated to deep-learning models for NLP.
- Introduction to NLP, the main tasks, issues and peculiarities - Sequence tagging: models and applications - Computational Semantics - Syntax and Parsing - Deep Learning for NLP: introduction and basics - Deep Learning for NLP: advanced architectures - Deep Learning for NLP: Machine translation, a case study
Pré-requis recommandés :
pytorch
Compétences à acquérir :
- Skills in Natural Language Processing using deep-learning
- Understand new architectures
Bibliographie-lectures recommandées
References - Costa-jussà, M. R., Allauzen, A., Barrault, L., Cho, K., & Schwenk, H. (2017). Introduction to the special issue on deep learning approaches for machine translation. Computer Speech & Language, 46, 367-373. - Dan Jurafsky and James H. Martin. Speech and Language Processing (3rd ed. draft): web.stanford.edu/~jurafsky/slp3/ - Yoav Goldberg. A Primer on Neural Network Models for Natural Language Processing: u.cs.biu.ac.il/~yogo/nnlp.pdf - Ian Goodfellow, Yoshua Bengio, and Aaron Courville. Deep Learning: www.deeplearningbook.org
- Deep learning for image analysis
Deep learning for image analysis
Ects : 4
Enseignant responsable :
- Etienne DECENCIERE
Volume horaire : 24
Description du contenu de l'enseignement :
Deep learning has achieved formidable results in the image analysis field in recent years, in many cases exceeding human performance. This success opens paths for new applications, entrepreneurship and research, while making the field very competitive.
This course aims at providing the students with the theoretical and practical basis for understanding and using deep learning for image analysis applications.
Program to be followed The course will be composed of lectures and practical sessions. Moreover, experts from industry will present practical applications of deep learning. Lectures will include:
- Artificial neural networks, back-propagation algorithm - Convolutional neural networks - Design and optimization of a neural architecture - Analysis of neural network function - Image classification and segmentation - Auto-encoders and generative networks - Transformers - Current research trends and perspectives
During the practical sessions, the students will code in Python, using Keras or Pytorch. They will be confronted with the practical problems linked to deep learning: architecture design; optimization schemes and hyper-parameter selection; analysis of results.
Pré-requis obligatoire :
- Linear algebra, basic probability and statistics
- Python
Compétences à acquérir :
Deep learning for image analysis: theoretical foundations and applications
Mode de contrôle des connaissances :
Practical session and exam
- Data acquisition, extraction and storage
Data acquisition, extraction and storage
Ects : 4
Enseignant responsable :
Volume horaire : 24
Description du contenu de l'enseignement :
The objective of this course is to present the principles and techniques used to acquire, extract, integrate, clean, preprocess, store, and query datasets, that may then be used as input data to train various artificial intelligence models. The course will consist on a mix of lectures and practical sessions. We will cover the following aspects:
- Web data acquisition (Web crawling, Web APIs, open data, legal issues)
- Information extraction from semi-structured data
- Data cleaning and data deduplication
- Data formats and data models
- Storing and processing data in databases, in main memory, or in plain files
- Introduction to large-scale data processing with MapReduce and Spark
- Ontology-based data access
Pré-requis obligatoire :
Basics of computer science and computer engineering (algorithms, databases, programming, logics, complexity).
Compétences à acquérir :
Understanding:
- how to acquire data from a variety of sources and in a variety of formats
- how to extract structured data from unstructured or semi-structured data
- how to format, integrate, clean data sets
- how to store and access data sets
Mode de contrôle des connaissances :
Project (50% of the grade) and in-class written assessment (50% of the grade)
En savoir plus sur le cours :
Cours optionnels - 5 cours à choisir parmi :
- Advanced machine learning
Advanced machine learning
Ects : 4
Enseignant responsable :
Volume horaire : 24
Description du contenu de l'enseignement :
This research-oriented module will focus on advanced machine learning algorithms, in particular in the Bayesian setting 1) Bayesian Machine Learning (with Moez Draief, chief data scientist CapGemini) - Bayesian linear regression - Gaussian Processes (i.e. kernelized Bayesian linear regression) - Approximate Bayesian Inference - Latent Dirichlet Allocation 2) Bayesian Deep Learning (with Julyan Arbel, CR INRIA) - MCMC methods - variationnal methods 3) Advanced Recommandation Techniques (with Clement Calauzene, Criteo)
Compétences à acquérir :
Probabilistic, Bayesian ML and recommandation systems
Mode de contrôle des connaissances :
- Chaque étudiant devra présenter un papiers de recherche
- Computational social choice
Computational social choice
Ects : 4
Enseignant responsable :
Volume horaire : 24
Description du contenu de l'enseignement :
The aim of this course is to give an overview of the problems, techniques and applications of computational social choice, a multidisciplinary topic at the crossing point of computer science (especially artificial intelligence, operations research, theoretical computer science, multi-agent systems, computational logic, web science) and economics. The course consists of the analysis of problems arising from the aggregation of preferences of a group of agents from a computational perspective. On the one hand, it is concerned with the application of techniques developed in computer science, such as complexity analysis or algorithm design, to the study of social choice mechanisms, such as voting procedures or fair division algorithms. On the other hand, computational social choice is concerned with importing concepts from social choice theory into computing. The course will focus on normative aspects, computational aspects, and real-world applications (including some case studies). Program: 1. Introduction to social choice and computational social choice. 2. Preference aggregation, Arrow's theorem and how to escape it. 3. Voting rules: informational basis and normative aspects. 4. Voting rules : computation. Voting on combinatorial domains. 5. Strategic issues: strategyproofness, Gibbard and Satterthwaite's theorem, computational resistance to manipulation, other forms of strategic behaviour. 6. Multiwinner elections. Public decision making and participatory budgeting. 7. Communication issues in voting: voting with incomplete preferences, elicitation protocols, communication complexity, low-communication social choice. 8. Fair division. 9. Matching under preferences. 10. Specific applications and case studies (varying every year): rent division, kidney exchange, school assignment, group recommendation systems …
Pré-requis recommandés :
Prerequisite-free. Basics of discrete mathematics (especially graph theory) and algorithmics is a plus.
Pré-requis obligatoire :
none
Compétences à acquérir :
N/S
Mode de contrôle des connaissances :
Written exam by default.
Bibliographie-lectures recommandées
References: * Handbook of Computational Social Choice (F. Brandt, V. Conitzer, U. Endriss, J. Lang, A. Procaccia, eds.), Cambridge University Press, 2016. Available for free online. * Trends in Computational Social Choice (U. Endriss, ed), 2017. Available for free online.
- Science des données au Collège de France
Science des données au Collège de France
Ects : 4
- Graph analytics
Graph analytics
Ects : 4
Enseignant responsable :
Volume horaire : 24
Description du contenu de l'enseignement :
The objective of this course course is to give students an overview of the field of graph analytics. Since graphs form a complex and expressive data type, we need methods for representing graphs in databases, manipulating, querying, analyzing and mining them. Moreover, graph applications are very diverse and need specific algorithms. The course presents new ways to model, store, retrieve, mine and analyze graph-structured data and some examples of applications. Lab sessions are included allowing students to practice graph analytics: modeling a problem into a graph database and performing analytical tasks over the graph in a scalable manner.
Program • Graph analytics – Networks properties and models – Link Analysis : PageRank and its variants – Community detection • Frameworks for parallel graph analytics – Pregel – a model for parallel-graph computing – GraphX Spark – unifying graph- and data – parallel computing • Machine learning with graphs • Applications : process mining and analysis Practical work : graph analytics with GraphX and Neo4J
Compétences à acquérir :
Modeling a problem into a graph model and performing analytical tasks over the graph in a scalable manner.
Bibliographie-lectures recommandées
References
Ian Robinson, Jim Weber, Emil Eifrem, Graph Databases, Editeur : O'Reilly (4 juin 2013), ISBN-10: 1449356265
Eric Redmond, Jim R. Wilson, Seven Databases in Seven Weeks - A Guide to Modern Databases and the NoSQL Movement, Publisher: Pragmatic Bookshelf
Grzegorz Malewicz, Matthew H. Austern, Aart J.C Bik, James C. Dehnert, Ilan Horn, Naty Leiser, and Grzegorz Czajkowski. 2010. Pregel: a system for large-scale graph processing, SIGMOD '10, ACM, New York, NY, USA, 135-146
Xin, Reynold & Crankshaw, Daniel & Dave, Ankur & Gonzalez, Joseph & J. Franklin, Michael & Stoica, Ion. (2014). GraphX: Unifying Data-Parallel and Graph-Parallel Analytics.
Michael S. Malak and Robin East, Spark GraphX in Action, Manning, June 2016
- Incremental learning, game theory and applications
Incremental learning, game theory and applications
Ects : 4
Enseignant responsable :
Volume horaire : 24
Description du contenu de l'enseignement :
This course will focus on the behavior of learning algorithms when several agents are competing against one another: specifically, what happens when an agent that follows an online learning algorithm interacts with another agent doing the same? The natural language to frame such questions is that of game theory, and the course will begin with a short introduction to the topic, such as normal form games (in particular zero-sum, potential, and stable games), solution concepts (such as dominated/rationalizable strategies, Nash, correlated and coarse equilibrium notions, ESS), and some extensions (Blackwell approachability). Subsequently, we will examine the long-term behavior of a wide variety of online learning algorithms (fictitious play, regret-matching, multiplicative/exponential weights, mirror descent and its variants, etc.), and we will discuss applications to generative adversarial networks (GANs), traffic routing, prediction, and online auctions. [1] Nicolò Cesa-Bianchi and Gábor Lugosi, Prediction, learning, and games, Cambridge University Press, 2006. [2] Drew Fudenberg and David K. Levine, The theory of learning in games, Economic learning and social evolution, vol. 2, MIT Press, Cambridge, MA, 1998. [3] Sergiu Hart and Andreu Mas-Colell, Simple adaptive strategies: from regret matching to uncoupled dynamics, World Scientific Series in Economic Theory - Volume 4, World Scientific Publishing, 2013. [4] Vianney Perchet, Approachability, regret and calibration: implications and equivalences, Journal of Dynamics and Games 1 (2014), no. 2, 181 – 254. [5] Shai Shalev-Shwartz, Online learning and online convex optimization, Foundations and Trends in Machine Learning 4 (2011), no. 2, 107 – 194.
Compétences à acquérir :
Learning procedures when several agents are playing against one-other
- Knowledge graphs, description logics, reasoning on data
Knowledge graphs, description logics, reasoning on data
Ects : 4
Enseignant responsable :
- Michael THOMAZZO
Volume horaire : 24
Description du contenu de l'enseignement :
Introduction to Knowledge Graphs, Description Logics and Reasoning on Data. Knowledge graphs are a flexible tool to represent knowledge about the real world. After presenting some of the existing knowledge graphs (such as DBPedia, Wikidata or Yago) , we focus on their interaction with semantics, which is formalized through the use of so-called ontologies. We then present some central logical formalism used to express ontologies, such as Description Logics and Existential Rules. A large part of the course will be devoted to study the associated reasoning tasks, with a particular focus on querying a knowledge graph through an ontology. Both theoretical aspects (such as the tradeoff between the expressivity of the ontology language versus the complexity of the reasoning tasks) and practical ones (efficient algorithms) will be considered.
Program: 1. Knowledge Graphs (history and uses) 2. Ontology Languages (Description Logics, Existential Rules) 3. Reasoning Tasks (Consistency, classification, Ontological Query Answering) 4. Ontological Query Answering (Forward and backward chaining, Decidability and complexity, Algorithms, Advanced Topics)
References: -- The description logic handbook: theory, implementation, and applications. Baader et al., Cambridge University Press -- Foundations of Semantic Web Technologies, Hitzler et al., Chapman&Hall/CRC -- Web Data Management, Abiteboul et al., Cambridge University Press Prerequisites: -- first-order logic; -- complexity (Turing machines, classical complexity classes) is a plus.
- Machine learning on Big Data
Machine learning on Big Data
Ects : 4
Enseignant responsable :
Volume horaire : 24
Description du contenu de l'enseignement :
This course focuses on the typical, fundamental aspects that need to be dealt with in the design of machine learning algorithms that can be executed in a distributed fashion, typically on Hadoop clusters, in order to deal with big data sets, by taking into account scalability and robustness. Nowadays there is an ever increasing demand of machine learning algorithms that scales over massives data sets. In this context, this course focuses on the typical, fundamental aspects that need to be dealt with in the design of machine learning algorithms that can be executed in a distributed fashion, typically on Hadoop clusters, in order to deal with big data sets, by taking into account scalability and robustness. So the course will first focus on a bunch of main-stream, sequential machine learning algorithms, by taking then into account the following crucial and complex aspects. The first one is the re-design of algorithms by relying on programming paradigms for distribution and parallelism based on map-reduce (e.g., Spark, Flink, … .). The second aspect is experimental analysis of the map-reduce based implementation of designed algorithms in order to test their scalability and precision. The third aspect concerns the study and application of optimisation techniques in order to overcome lack of scalability and to improve execution time of designed algorithm.
The attention will be on machine learning technique for dimension reduction, clustering and classification, whose underlying implementation techniques are transversal and find application in a wide range of several other machine learning algorithms. For some of the studied algorithms, the course will present techniques for a from-scratch map-reduce implementation, while for other algorithms packages like Spark ML will be used and end-to-end pipelines will be designed. In both cases algorithms will be analysed and optimised on real life data sets, by relaying on a local Hadoop cluster, as well as on a cluster on the Amazon WS cloud.
References:
- Mining of Massive Datasets www.mmds.org
- High Performance Spark - Best Practices for Scaling and Optimizing Apache Spark Holden Karau, Rachel Warren O'Reilly
- Mathematics of deep learning
Mathematics of deep learning
- Monte-Carlo search and games
Monte-Carlo search and games
Ects : 4
Enseignant responsable :
Volume horaire : 24
Description du contenu de l'enseignement :
Introduction to Monte Carlo for computer games. Monte Carlo Search has revolutionized computer games. It works well with Deep Learning so as to create systems that have superhuman performances in games such as Go, Chess, Hex or Shogi. It is also appropriate to address difficult optimization problems. In this course we will present different Monte Carlo search algorithms such as UCT, GRAVE, Nested Monte Carlo and Playout Policy Adaptation. We will also see how to combine Monte Carlo Search and Deep Learning. The validation of the course is a project involving a game or an optimization problem. La recherche Monte-Carlo a révolutionné la programmation des jeux. Elle se combine bien avec le Deep Learning pour créer des systèmes qui jouent mieux que les meilleurs joueurs humains à des jeux comme le Go, les Echecs, le Hex ou le Shogi. Elle permet aussi d ’ approcher des problèmes d ’ optimisation difficiles. Dans ce cours nous traiterons des différents algorithmes de recherche Monte-Carlo comme UCT, GRAVE ou le Monte-Carlo imbriqué et l ’ apprentissage de politique de playouts. Nous verrons aussi comment combiner recherche Monte-Carlo et apprentissage profond. Le cours sera validé par un projet portant sur un jeu ou un problème d ’ optimisation difficile.
Bibliographie-lectures recommandées
Bibliographie : Intelligence Artificielle Une Approche Ludique, Tristan Cazenave, Editions Ellipses, 2011.
- No SQL databases
No SQL databases
Ects : 4
Enseignant responsable :
- PAUL BONIOL
Volume horaire : 24
- Fairness, privacy and robustness for trustworthy machine learning
Fairness, privacy and robustness for trustworthy machine learning
- Large dimensional statistics
Large dimensional statistics
- Optimal transport (ENS)
Optimal transport (ENS)
Ects : 4
Volume horaire : 24
Description du contenu de l'enseignement :
Optimal transport (OT) is a fundamental mathematical theory at the interface between optimization, partial differential equations and probability. It has recently emerged as an important tool to tackle a surprisingly large range of problems in data sciences, such as shape registration in medical imaging, structured prediction problems in supervised learning and training deep generative networks. This course will interleave the description of the mathematical theory with the recent developments of scalable numerical solvers. This will highlight the importance of recent advances in regularized approaches for OT which allow one to tackle high dimensional learning problems.
The course will feature numerical sessions using Python.
- Motivations, basics of probabilistic modeling and matching problems.
- Monge problem, 1D case, Gaussian distributions.
- Kantorovitch formulation, linear programming, metric properties.
- Shrödinger problem, Sinkhorn algorithm.
- Duality and c-transforms, Brenier’s theory, W1, generative modeling.
- Semi-discrete OT, quantization, Sinkhorn dual and divergences
- Bayesian inference
Bayesian inference
PSL Week
Bloc stage
- Stage
Stage
Ects : 10
Formation année universitaire 2025 - 2026 - sous réserve de modification
Modalités pédagogiques
A partir de janvier 2025, les cours seront dispensés au 16 bis rue de l'Estrapade, 75005 Paris.
Le Master IASD se compose d’un semestre d’enseignements avancés sur les disciplines de l’IA (de septembre à décembre) suivi d’un semestre d’options (de janvier à avril) et d’un stage de recherche (d’avril à septembre). Les cours sont divisés en deux semestres. Pendant le premier semestre de tronc commun, de septembre à décembre, les étudiant(e)s doit suivre six cours d’intelligence artificielle et de science des données. Pendant le second semestre de cours optionnels, de janvier à avril, les étudiant(e)s doivent choisir au minimum six cours d’approfondissement parmi une large sélection d’options. Le stage, d’avril à août est effectué dans un laboratoire de recherche académique ou industriel et se conclut par la rédaction d’un mémoire et une soutenance courant septembre. Pour les étudiant(e)s qui en ont besoin, des cours de mise à niveau sur les fondements mathématiques et informatique sont programmés avant le début des cours de tronc commun en septembre.
Les enseignements sont assurés par des chercheurs actifs dans le domaine et abordent les différents aspects de l’IA d’aujourd’hui : apprentissage automatique, représentation des connaissances, gestion et fouille de grande masses de données, paradigmes du Big Data. En plus des enseignements fondamentaux, les étudiantes et les étudiants peuvent personnaliser leur cursus en choisissant 6 cours en plus parmi un large éventail d’options.
Les étudiantes et les étudiants inscrits en 2e année de Master Informatique parcours IASD peuvent choisir au semestre 4 comme UE optionnelles des UE proposées en 2e année de Master Intelligence Artificielle, Systèmes, Données (IASD) - Mathématiques (anciennement MASH).
Cette démarche nécessite :
- Une demande écrite de l'étudiant(e).
- L'accord écrit de l'enseignant du cours concerné.
- Une réponse écrite de l'administration.
- Le respect des modalités de contrôle des connaissances du M2 IASD - Mathématiques pour l'évaluation des UE.
Stages et projets tutorés
Un stage de 5 mois est obligatoire, à partir du début du mois d’avril.
Liste des stages est disponible : ici
Pour les étudiantes et les étudiants : comment trouver un stage, et obtenir la convention.
Pour trouver un stage, vous pouvez consulter la liste des propositions de stage, ou démarcher vous mêmes les laboratoires ou entreprises qui vous intéressent. Puis, il vous faudra obtenir la validation pédagogique de votre sujet de stage. Pour l’obtenir, il faut charger votre sujet ici. En spécifiant en commentaire que le sujet de stage est pour vous. (Merci de ne pas envoyer votre sujet par mail.)
Lorsque vous avez obtenu la validation pédagogique, vous pouvez remplir le formulaire dans l’application ESUPstage pour obtenir votre convention de stage.
Pour plus d'info sur la présentation des stages.
Pour les encadrants : comment proposer un stage aux étudiantes et étudiants du master IASD ?
Si vous faites partie d’un laboratoire de recherche, ou d’un département R&D, vous pouvez proposer un sujet de stage en cliquant ici. Bien sûr, le stage doit être en relation avec l’un des sujets abordés dans le programme du master. Le stage apparaîtra dans la liste ci-dessous après validation par l’équipe pédagogique.
Gratification : En France, les stages de plus de 2 mois doivent s’accompagner d’une gratification. Plus d’informations et un outil pour calculer la rémunération des stagiaires sont disponibles à cette adresse:
https://www.service-public.fr/simulateur/calcul/gratification-stagiaire.
Des programmes nourris par la recherche
Les formations sont construites au contact des programmes de recherche de niveau international de Dauphine, qui leur assure exigence et innovation.
La recherche est organisée autour de 6 disciplines toutes centrées sur les sciences des organisations et de la décision.
En savoir plus sur la recherche à Dauphine