https://fall.metz.centralesupelec.fr/dir-000/index.html Hugo en-us https://fall.metz.centralesupelec.fr/dir-000/000/index.html Mon, 01 Jan 0001 00:00:00 +0000 https://fall.metz.centralesupelec.fr/dir-000/000/index.html Machine learning 1 ECTS: 3 Description: This course sets out the general framework of machine learning, allowing you to situate the different approaches in the field. It covers the notions of data pre-processing, an introduction to statistical learning theory (risks, overlearning, convex proxies, regularization), the difference between frequentist and Bayesian approaches, supervised, unsupervised, semi-supervised and reinforcement learning paradigms. Some approaches are detailed (Kernel methods, SVM, Boosting, Bagging, Decision trees…). https://fall.metz.centralesupelec.fr/dir-000/001/index.html Mon, 01 Jan 0001 00:00:00 +0000 https://fall.metz.centralesupelec.fr/dir-000/001/index.html Machine learning 2 ECTS: 2 Description: This course complements Machine Learning 1 with notions of data processing (dimension reduction, etc.), unsupervised learning, active and semi-supervised learning, explicability issues. Learning outcomes: By the end of this course, students will have completed their breadth approach to machine learning. Evaluation methods: 2h written test, can be retaken. Course supervisor: Arthur Hoarau https://fall.metz.centralesupelec.fr/dir-000/002/index.html Mon, 01 Jan 0001 00:00:00 +0000 https://fall.metz.centralesupelec.fr/dir-000/002/index.html Deep learning ECTS: 3 Description: Deep learning is a technology that is booming, thanks in particular to the use of GPUs (Graphical Processing Units), the availability of large amounts of data and the understanding of theoretical elements that make it possible to better define neural network architectures that are more easily trainable. In this course, students will be introduced to the basics of neural networks and also to the different architectural elements that make it possible to design a neural network according to the prediction problem considered. The course is divided into modules in which questions of optimization algorithms, their initialization, regularization techniques, fully connected architectures, convolutional networks, recurrent networks, introspection techniques are addressed. Practical works on GPUs are associated with the courses. https://fall.metz.centralesupelec.fr/dir-000/003/index.html Mon, 01 Jan 0001 00:00:00 +0000 https://fall.metz.centralesupelec.fr/dir-000/003/index.html Statistical Models 1 ECTS: 3 Description: The “Statistical Modeling” courses ModStat1 and ModStat2 deal with the modeling of systems for which the outputs are sufficiently uncertain that they need to be modeled by random variables. The course begins with a review of statistics and the introduction of elementary models (e.g. naive Bayes, linear regression, etc.), moving progressively towards more complex models. While the courses present the most useful elementary models and methods in this modeling context, they are not intended to be an exhaustive catalog. The aim is rather to present, within a consistent theory, the concepts and tools common to all these models and methods, and to show how, starting from modeling hypotheses specific to each concrete problem, these concepts are logically assembled before leading to an operational method. From a practical point of view, the aim of this course is not only to give students the means to understand and make good use of existing model implementations, but also to design their own implementations to take into account the specificities of a given problem. The course focuses on linking theory to practice: first, the hypotheses associated with a given class of problems are identified in class, followed by theoretical modeling work, leading to the definition of a model and its estimation algorithms. These results are then applied to a case study in tutorial sessions, before being implemented (in Python) and evaluated on data in practical exercises. The ModStat1 course will introduce the basic tools of statistical modeling, while the ModStat2 course will focus on hidden variable models. https://fall.metz.centralesupelec.fr/dir-000/004/index.html Mon, 01 Jan 0001 00:00:00 +0000 https://fall.metz.centralesupelec.fr/dir-000/004/index.html Statistical models 2 ECTS: 3 Description: This course is an extension of the ModStat1 course. It is structured around the three fundamental concepts of stochastic processes, latent variables and approximate inference techniques. The first part of the course on processes focuses on three main families of processes: point processes, Markov processes and Gaussian processes. The notion of latent variable is then addressed through mixture models and the EM algorithm. The two notions are then combined to develop hidden Markov models, for both discrete (HMM) and continuous states (Kalman filters). Finally, approximate inference techniques are presented, with sampling techniques (MCMC) and variational inference.