• Réunion de pré-rentrée : 27 septembre, 14h
  • Premier semestre (tronc commun) : 30 septembre au 20 décembre
    Examens premier semestre : semaine du 6 janvier
  • Second semestre : 13 janvier au 20 mars
    Examen second semestre : semaine du 23 mars
  • PSL Master Course on Digital Humanities & AI: semaine du 30 mars (optionnel)
  • Stage : 5 mois entre avril à septembre
  • Rattrapage premier semestre : pendant le mois d’avril
  • Rattrapage second semestre : de mi-août à mi-septembre
  • Date limite de rendu du mémoire de stage : 6 septembre
  • Soutenances : semaine du 21 septembre

Tronc Commun :

6 ECTS. (En anglais.) The aim of this course is to provide the students with the fundamental concepts and tools for developing and analyzing machine learning algorithms. 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: — Introduction to the different paradigms of ML and applications
  • Bayes rule, MLE, MAP
  • the fully bayesian setting
  • Computational learning theory
  • Empirical Risk Minimization
  • Universal consistency
  • ERM and ill-posed problems
  • biais-variance tradeoff
  • Regularization
  • PAC model and Sample complexity
  • MDL and Sample ...
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6 ECTS. (En anglais.) This course will review the mathematical foundations for Machine Learning, as well as the underlying algorithmic methods and showcases some modern applications of a broad range of optimization techniques. Optimization is at the heart of most recent advances in machine learning. This includes of course most basic methods (linear regression, SVM and kernel methods). It is also the key for the recent explosion of deep learning which are state of the art approaches to solve supervised and unsupervised problems in imaging, vision and natural language processing. This course will review the mathematical foundations, the underlying algorithmic ...
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6 ECTS. (En anglais.) The first goal of the course is to enable students to acquire strong skills for the efficient querying of relational databases. The second goal of the course is to present foundations and advanced techniques supporting systems for semi-structured data processing. it is well known that in the context of data processing for IA applications, a large part of the effort is devoted to data preparation, a process that strongly depends on techniques and skills for formulating complex queries and tuning systems supporting their execution, in order to ensure reasonable query execution time. In a wide range ...
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4 ECTS. (En anglais.) The course introduces techniques for representing and reasoning over knowledge information. 1. Reasoning about Belief, Knowledge, and Preferences – plausible and nonmonotonic reasoning
– reasoning about belief and knowledge (single-and multiple-agent), belief change
– case-based reasoning, analogical reasoning
– preference languages, reasoning about preferences
– reasoning and decision under uncertainty, graphical models 2. Reasoning about Action and Planning – reasoning about action, action languages for planning
– algorithms for classical planning and hierarchical planning
– planning under uncertainty
– multi-agent planning
– planning and search ...
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4 ECTS (En anglais.) 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. 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 ...
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4 ECTS. (En anglais) Introduction to Deep Learning. Deep Learning enable to train neural network with many layers so as to address various difficult problems. Applications range from image to games. In this course we will present Stochastic Gradient Descent for deep neural networks using different architectures (convolutions, dense, recurrent, residual). We will use Keras/Tensorflow and/or Pytorch and apply them to games and optimization. References: Deep Learning avec TensorFlow – Mise en oeuvre et cas concrets – 22 novembre 2017
de Aurélien Géron, O’Reilly. Keras Documentation : Pytorch Documentation : ...
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Options :

Note: toutes les options comptent 3 ECTS. Chaque étudiant doit choisir au minimum 6 options, soit 18 ECTS.

Les slides de la journée de présentation des options sont disponibles ici.

  • 3 ECTS
  • Langue du cours : Anglais
The main aim of this course is to give students a deep and solid understanding of the state of the art of Big Data systems and programming paradigms, and to enable them to devise and implement efficient algorithms for analysing massive data sets. The focus will be on paradigms based on distribution and shared-nothing parallelism, which are crucial to enable the implementation of algorithms that can be run on clusters of computers, scale as the size of input data increases, and can be safely executed even in the presence of system ...
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(en Anglais) This research-oriented module will focus on advanced machine learning algorithms Probabilistic and Bayesian Machine Learning
Bayesian linear regression
Gaussian Processes (i.e. kernelized Bayesian linear regression)
Approximate Bayesian Inference
Latent Dirichlet Allocation
Beyond the supervised/unsupervised learning problems
Semi-supervised learning
Density-gap based methods
Manifold based methods
Active learning
Selective sampling
Disagreement region coefficient
Sparse models
Advanced Deep learning Techniques
Generative Learning
Denoising auto-encoders
Variational Auto-Encoders
Learning representations
Word2vec, graph embeddings, …
Randomized algorithms
Random projections, LSH
Primal methods for kernel classifiers (random kitchen sinks) ...
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(en Anglais)
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 ...
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(en Anglais)
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 network
• Design ...
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(en Anglais)
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 ...
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Ce cours donne un panorama des concepts et techniques d’acquisition, de traitement et de visualisation des nuages de points 3D, et de leurs fondements mathématiques et algorithmiques. Aller sur la page Web du cours ...
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Le cours a pour objectif de sensibiliser un public essentiellement technique (informaticiens), dans un contexte pluri-disciplinaire :
  • aux transformations profondes en cours avec la révolution numérique
  • aux cadres juridiques et règlementaires déjà existants et comment les respecter
  • aux questions éthiques ouvertes, et à la réflexion éthique pour des questions à venir
Accès à la page Web du cours ...
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(En anglais.) 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 ...
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(En anglais.) The objective of this course course is to give students an overview of the field of graph analytics. 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: ...
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(En anglais.)
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., ...
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(En anglais.) 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 ...
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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 ...
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(En anglais.) This introductory course will provide the main methodological building blocks of reinforcement learning. Reinforcement Learning (RL) refers to situations where the learning algorithm operates in close-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 ...
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La description de ce cours n’est pas encore disponible ...
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