• Mathematically model data and physical processes, and be able to extract relevant information and knowledge through statistical tools.
• Develop a learning model from data, with honest testing that takes computational limitations into account.

Ekhine IRUROZKI ARRIETA

• Build new non-parametric estimators in situations not covered in class.
• Critique techniques used with respect to their complexity and intrinsic performance.

Philippe CIBLAT

• Explain the fundamental concepts of statistical learning and relate them to the essential choices (representation, modeling, optimization, evaluation) to be made when facing a given problem.
• Explain the models and the learning algorithms dedicated to them, the reasoning behind their construction, and compare them with each other.
• Implement methods (neural or not) on supervised classification or regression tasks; in particular, evaluate them.

Pascal BIANCHI

• Calculate the gradient of the loss of a generalized linear model.
• Manipulate convex inequalities in order to prove the convergence of an algorithm.
• Identify the properties of an optimization problem in order to choose an appropriate resolution method.
• Reformulate an optimization problem using Lagrangian duality.
• Implement an optimization algorithm using a closed form gradient or automatic differentiation.

Radu-Alexandru DRAGOMIR, Olivier FERCOQ

• Explain the difficulties associated with processing textual language data and the related tasks.
• Explain methods of numerical representation of text, and basic methods of text processing for classical tasks.
• Describe the elements leading to the successful use of neural models for the representation and processing of written language.
• Implement methods (traditional and early neural approaches) on a range of simple tasks, using Python and specialized libraries (NLTK, Gensim, Scikit-learn).

Matthieu LABEAU

• Explain the theory underlying the main architectures and learning paradigms of deep learning.
• Program these architectures in Python using the PyTorch deep learning framework.
• Apply these architectures to typical computer vision, NLP, or audio problems such as classification, regression, or generation.

Geoffroy PEETERS

• Basic understanding of modern database systems, their function, and implementation, and be able to model databases.
• Formulate queries over databases using SQL, create and update databases using SQL, understand the concepts of query processing and optimization.

Mehwish ALAM

• Model complex systems using graphs.
• Apply learning techniques on graphs.
• Interpret the results obtained.

Thomas BONALD

• Compute the conditional expectation of a random variable given a sigma-algebra.
• Describe and compute the conditional distribution of a random variable given a sigma-algebra or another random variable.
• Recognize and use a sufficient statistic to estimate a parameter.
• Define, recognize and study a martingale, in particular its asymptotic behavior.

Francois ROUEFF

• Characterize a Markov chain and its kernel.
• Compute conditional expectations and probabilities of future events or statistics given the past by exploiting the Markov property, including when this future occurs at random times.

Francois ROUEFF

• Understand phenomena related to high dimension.
• Master fundamental probabilistic tools for the analysis of learning algorithms.
• Implement central mathematical techniques in preparation for a Master 2 in statistics and data science.

Yann ISSARTEL

• Build a quadrature method of any order.
• Define basic and importance sampling Monte Carlo methods.
• Choose an ODE method adapted to the problem at stake.
• Compare the numerical methods both from their theoretical properties and their experimental performance.
• Prove convergence by splitting the global error into consistence and stability errors.

Stephan CLEMENÇON, Olivier FERCOQ

• Model physical phenomena or noisy time signals using stochastic differential equations.
• Evaluate functionals of solutions of stochastic differential equations.
• Simulate solutions of stochastic differential equations.

Laurent DECREUSEFOND

• Define a probabilistic model of a discrete-event phenomenon.
• Define a probabilistic model of a system with a spatial component.
• Evaluate the performance of the previously modeled systems.

Laurent DECREUSEFOND

• Describe the processing cycle of a web page, the HTTP protocol, its handling by a browser, its internal representation (the DOM), and its presentation to the user.
• Develop programs that interface with this ecosystem: on the server and in a browser.
• Use the basic libraries of web programming.
• Search for new libraries specialized for particular application domains, interpret their documentation, context, and feasibility for a given problem.

James EAGAN, Théo ZIMMERMANN

• Solve linear systems and determine the LU decomposition of matrices.
• Determine the eigenvalues and eigenvectors of symmetric matrices.
• Model and solve linear optimization problems using the simplex algorithm. Express the dual problem of a linear optimization problem and use the existing links between the primal and dual problems.
• Design and apply gradient methods to solve nonlinear optimization problems with or without constraints, in one or several dimensions.
• Identify points of the considered domain satisfying necessary or sufficient conditions for local optimality or, in the convex case, for global optimality.

Olivier HUDRY

• Identify useful/relevant resources and their veracity in order to build their analysis.
• Sort and use these resources to complement the different tools presented in class.
• Analyze and interpret the observations resulting from the analysis tools.
• Produce and justify strategic recommendations for one of the market players.

Myriam DAVIDOVICI

• Interpret the main indicators of finance and accounting.
• Analyze a balance sheet, investments and depreciation of a company.
• Explain inventory and how salaries work.
• Assess inflation and corporate debt.
• Explain corporate income tax.

Alexis Joulie

• Understand and situate the "human factor" within complex socio-technical systems.
• Describe and explain the main cognitive processes of humans.
• Identify the benefits of taking humans into account in design processes, and describe and critique the main strategies for doing so.
• Identify the main ethical issues related to behavior measurement and the integration of users in design.
• Identify the courses that further explore these themes (TSE, SES, HC).

Panayotis Mavros

• Define the main concepts of sociology such as identity, community, social capital, norm, privacy, public sphere, and social structures.
• Identify and name social dynamics within social networks and their impact on organizations.
• Analyze personal use of technology with critical distance.
• Use sociological concepts to demystify Internet-related myths.
• Justify professional decisions based on a deep understanding of the mechanisms of social appropriation of technologies.

Antonio CASILLI

An introduction to contemporary philosophy themes renewed by empirical findings from biology and psychology, examining both the underlying research and the questions it raises. No prior knowledge required; the course aims to equip students with concepts to understand modern debates. Topics covered this year:

• Identity in peril: from the Ship of Theseus to schizophrenia.
• Is morality a product of natural Evolution? From cooperation among vampires to Frans de Waal's monkeys.
• How is submission produced? From Stanley Milgram's experiments to the Panopticon.
• Why are we afraid of inferiority? From Tocqueville to the rankings of the Grandes Écoles.
• Is death an ephemeral invention of life? From August Weismann to transhumanism.

Alexis ROSEMBAUM

• English: Treasure Hunting (S1), Socio-Linguistics (S1), The Wire (S2).
• Spanish: ES0 — A2 (S1 & S2). Level B1.
• Intercultural Tandem (S2, elective) — a semester-long language exchange with a Télécom Paris student from Singapore, alternating conversation practice in each other's language.

Projets

• PROJ103 — First-semester group project: ARTEFACT.
  PROJ103 — Projet de groupe du semestre 1 : ARTEFACT.
• PROJ104 — Second-semester application project: Artishow.
  PROJ104 — Projet d'application du semestre 2 : Artishow.

Mathématiques Appliquées

• Functional and Fourier analysis.
  Analyse fonctionnelle et de Fourier.
• Probability.
  Probabilités.
• Digital communications and information theory.
  Communications numériques et théorie de l'information.
• Propagation and antennas in radio systems.
  Propagation et antennes dans les systèmes radio.
• Optics and photonics.
  Optique et photonique.

Informatique

• Algorithmics and foundations of computer science.
  Algorithmique et fondements de l'informatique.
• Networks.
  Réseaux.
• Web programming in TypeScript.
  Programmation web en TypeScript.
• From logic gates to operating systems.
  De la porte logique au système d'exploitation.
• Tools and applications for signal, image, and sound processing.
  Outils et applications pour le signal, les images et le son.

Sciences Economiques et Sociales

• Introduction to contemporary economics.
  Introduction à l'économie contemporaine.
• Written communication practices and analysis.
  Pratiques et analyses de communication écrite.
• Technology & society.
  Technologies & société.
• Introduction to management: business simulation game.
  Introduction au management : jeu d'entreprise.
• Entrepreneurship and digital innovation.
  Entrepreneuriat et innovation numérique.

Physique, Electronique

• Electronics for acquisition and computing systems.
  Electronique des systèmes d'acquisition et de calcul.
• Introduction to quantum technologies.
  Introduction aux technologies quantiques.
• Making light speak.
  Faire parler la lumière.

• English (Linguaskill C1 Certification; Building Confidence and Fostering Autonomy; Using English Internationally).
• Mandarin Chinese (ZH0–A2).

First year: Mathematics, Physics, Engineering Sciences.

• Mathematics: linear algebra, real analysis, series, elementary probability, geometry.
• Physics: point mechanics, electrokinetics, thermodynamics, optics.
• Engineering Sciences: mechanical systems, kinematics, introduction to automatic control.
• Computer Science: algorithmics and programming in Python.

Second year, star track: advanced Physics and Engineering Sciences.

• Mathematics: further algebra and analysis, probability.
• Physics: electromagnetism, fluid mechanics, advanced thermodynamics, wave physics.
• Engineering Sciences: automatic control, energetics, mechanics, electronics.
• Star track (étoile): reinforced program preparing for the most selective engineering school entrance exams.