Intelligence Artificielle, Systèmes, Données - 2ème année de Master

Ects : 6

Enseignant responsable :
YANN CHEVALEYRE

Volume horaire : 48

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 IntroSurrogate LossesUniform Convergence and PAC Learning Empirical Risk Minimization and ill-posed problemsConcentration InequalitiesUniversal consistency, PAC LearnabilityVC dimensionRademacher complexityNon Uniform Learning and Model Selection biais-variance tradeoffStructural Minimization Principle and Minimum Description Length PrincipleRegularization
• Part 2: Supervised Learning Theory and Algorithms in the Online Setting Foundations of Online LearningBeyond the Perceptron algorithm
• Partie 3: Ensemble Methods and Kernels Methods SVMs, KernelsKernel approximation algorithms in the primalEnsemble methods: bagging, boosting, gradient boosting, random forests
• Partie 4: Algorithms for Unsupervised Learning Dimensionality reduction: PCA, ICA, Kernel PCA, ISOMAP, LLERepresentation LearningExpectation 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.

Ects : 6

Enseignant responsable :
GABRIEL PEYRE

Volume horaire : 48

Description du contenu de l'enseignement :

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 methods and showcases some modern applications of a broad range of optimization techniques.

The course will be composed of both classical lectures and numerical sessions in Python. The first part covers the basic methods of smooth optimization (gradient descent) and convex optimization (optimality condition, constrained optimization, duality). The second part will features more advanced methods (non-smooth optimization, SDP programming,interior points and proximal methods). The last part will cover large scale methods (stochastic gradient descent), automatic differentiation (using modern python framework) and their application to neural network (shallow and deep nets).

Bibliographie-lectures recommandées

Theory and algorithms: Convex Optimization, Boyd and Vandenberghe Introduction to matrix numerical analysis and optimization, Philippe Ciarlet Proximal algorithms, N. Parikh and S. Boyd Introduction to Nonlinear Optimization - Theory, Algorithms and Applications, Amir Beck Numerics: Pyrthon and Jupyter installation: use only Python 3 with Anaconda distribution. The Numerical Tours of Signal Processing, Gabriel Peyré Scikitlearn tutorial #1 and Scikitlearn tutorial #2, Fabian Pedregosa, Jake VanderPlas Reverse-mode automatic differentiation: a tutorial Convolutional Neural Networks for Visual Recognition Christopher Olah, Blog  

Ects : 6

Enseignant responsable :
KHALID BELHAJJAME
DANIELA GRIGORI
DARIO COLAZZO

Volume horaire : 48

Description du contenu de l'enseignement :

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 of use cases, data preparation involves either structured or semi-structured datasets.

In this context, the first goal of the course is to enable students to acquire strong skills for the efficient querying of relational databases. After an initial refresh about the standard query language and the design of complex queries, the course will present optimization techniques that relational database management systems adopt in order to ensure efficient querying. The attention will be particularly given to storage and indexing techniques, as well as algorithms for the generation of efficient query execution plans, and main approach for database tuning. How these techniques are transposed in modern systems of the big data ecosystem will be also discussed.

The second goal of the course is to present foundations and advanced techniques supporting systems for semi-structured data processing. The attention is first focused formal specification of query languages, the design of complex queries as well as recent techniques for static analysis that help the user in the design of correct queries, in a context where this task is particularly difficult due to the potentially high and unpredictable variability in the structure of the datasets. Both parts will be supported by both books and scientific articles published in main conferences and journal on databases. The acquired notions will be consolidated in several lab-sessions.  

Compétences à acquérir :

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.

Bibliographie-lectures recommandées

Database Management Systems, Third Edition, Raghu Ramakrishnan, Johannes Gehrke. Mac Graw Hill Relational DBMS internals. Antonio Albano, Dario Colazzo, Giorgio Ghelli, Enzo Orsini. Book available in pdf.  

Ects : 4

Enseignant responsable :
GABRIELLA PIGOZZI
JEROME LANG

Volume horaire : 24

Description du contenu de l'enseignement :

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. Planning

- reasoning about action, action languages for planning

- algorithms for classical planning

- short introduction to decision theory and decision-theoretic planning

- planning under uncertainty and full observability: Markov decision processes

- planning under partial observabilty: partially observable Markov decision processes

- [time permitting] multi-agent planning

Pré-requis recommandés :

none

Pré-requis obligatoire :

none.

Compétences à acquérir :

N/A

Mode de contrôle des connaissances :

written exam.

Ects : 4

Enseignant responsable :
BENJAMIN NEGREVERGNE

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.

Ects : 4

Enseignant responsable :
TRISTAN CAZENAVE

Volume horaire : 24

Description du contenu de l'enseignement :

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 : keras.io Pytorch Documentation : pytorch.org  

Ects : 3

Enseignant responsable :
YANN CHEVALEYRE

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

Ects : 3

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.

Ects : 3

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 network • Design and optimization of a neural architecture • Successful architectures (AlexNet, VGG, GoogLeNet, ResNet) • Analysis of neural network function • Image classification and segmentation • Auto-encoders and generative networks • Current research trends and perspectives

During the practical sessions, the students will code in Python, using Keras and Tensorflow. 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: theoretical foundations and applications

Mode de contrôle des connaissances :

Exam

Ects : 3

Enseignant responsable :
ALEXANDRE ALLAUZEN

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

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

Ects : 3

Enseignant responsable :
Francois GOULETTE

Volume horaire : 24

Description du contenu de l'enseignement :

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.

Le cours abord notamment les thème suivants : Systèmes de perception 3D Traitements et opérateurs Recalage Segmentation de nuages de points Reconstruction de courbes et surfaces Modélisation par primitives Rendu de nuages de points et maillages

 

La plupart des séances sont complétées d'un TP.

Les cours sont en français, les sujets des TP sont en anglais.

Site Web : caor-mines-paristech.fr/fr/cours-npm3d/

Compétences à acquérir :

Compétences théoriques et pratiques sur la production, le traitement et les applications des nuages de points 3D.

Mode de contrôle des connaissances :

Comptes-rendus de TP.

Projet sur article.

Ects : 3

Enseignant responsable :
Francois GOULETTE

Volume horaire : 24

Description du contenu de l'enseignement :

Objectifs pédagogiques

 

Sensibiliser un public essentiellement technique (informaticiens) : – 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

 

Site Web : caor-mines-paristech.fr/fr/cours-eia/

Compétences à acquérir :

Compétences d'analyse des aspects éthiques, juridiques et techniques sur nouveaux produits et services utilisant l'Intelligence Artificielle.

Mode de contrôle des connaissances :

Projets sur des cas concrets de produits ou services utilisant de l'IA (ou nouvelles technologies au sens large), à réaliser en équipe.

Ects : 3

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.

Ects : 3

Enseignant responsable :
DANIELA GRIGORI

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

Ects : 3

Enseignant responsable :
DARIO COLAZZO

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

Ects : 3

Enseignant responsable :
JEROME LANG

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.

5. Strategic issues: strategyproofness, Gibbard and Satterthwaite's theorem, computational resistance to manipulation, other forms of strategic behaviour.

6. Voting on combinatorial domains.

7. Communication issues in voting: voting with incomplete preferences, elicitation protocols, communication complexity, low-communication social choice.

8. Fair division of indivisible goods.

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. Possibility to replace the exam by a project.

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.

Ects : 3

Enseignant responsable :
TRISTAN CAZENAVE

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.

Ects : 3

Volume horaire : 24

Description du contenu de l'enseignement :

1. Multiarmed bandits, Markov Decision Processes and other models 2. Planning: finite and infinite horizon problems, the value function, Bellman equations, dynamic programming, value and policy iteration 3. Probabilistic and statistical tools for RL: Bayesian models, relative entropy and hypothesis testing, concentration inequalities, linear regression, the stochastic approximation algorithm 4. RL algorithms for multiarmed bandits: the explore vs. exploit compromise, bandit algorithms vs. A/B testing, UCB, Thomson sampling, contextual bandits 5. RL algorithms for Markov Decision Processes: off policy and on policy learning, Q-learning, SARSA, Monte Carlo tree search

Compétences à acquérir :

This introductory course will provide the main methodological building blocks of reinforcement learning. 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 common scientific computing language.

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 J. Myles White. Bandit Algorithms for Website Optimization. O'Reilly. 2012 T. Lattimore and C. Szepesvari. Bandit Algorithms. Cambridge University Press. 2019. downloads.tor-lattimore.com/banditbook/book.pdf

Description du contenu de l'enseignement :

What you will learn in this class?

• Intro and Course Overview
• Supervised Learning behaviors
• Principles of Reinforcement Learning
• Policy Gradients
• Actor-Critic Algorithms (A2C, A3C and Soft AC)
• Value Function Methods
• Deep RL with Q-functions
• Advanced Policy Gradient (DDPG, Twin Delayed DDPG)
• Trust Region & Proximal Policy Optimization (TRPO, PPO)
• Optimal Control and Planning
• Model-Based Reinforcement Learning
• Model-Based Policy Learning
• Exploration and Stochastic Bandit in RL
• Exploration with Curiosity and Imagination
• Offline RL and Generalization issues
• Offline RL and Policy constraints

 

Why you should choose this course about DRL?

• DRL Is a very promising type of learning as it does not need to know the solution
• DRL Only needs the rules and good rewards
• DRL Combines the best aspects of deep learning and reinforcement learning.
• DRL has achieved impressive results in games, robotic, finance and many more fields

 

References

• Bertsekas, Dynamic Programming and Optimal Control, Vols I and II
• Goodfellow, Bengio, Deep Learning
• Powell, Approximate Dynamic Programming
• Puterman, Markov Decision Processes: Discrete Stochastic Dynamic Programming
• Sutton & Barto, Reinforcement Learning: An Introduction
• Szepesvari, Algorithms for Reinforcement Learning

Compétences à acquérir :

What you will acquire in this class?

• Understand principles of Deep Reinforcement Learning (DRL)
• Know main DRL algorithms
• Get some intuition about what DRL is good and not good at?
• Program DRL algorithms

Ects : 3

Enseignant responsable :
Pierre SENELLART

Volume horaire : 24

Ects : 3

Enseignant responsable :
Pierre SENELLART

Volume horaire : 24

Ects : 3

Volume horaire : 24

Ects : 3

Ects : 3

Ects : 12