[{"command":"insert","method":null,"selector":".course-modal-ajax-placeholder-959585","data":"\u003Cdiv class=\u0022container content-rte\u0022\u003E\n  \u003Cdiv class=\u0022row\u0022\u003E\n    \u003Cdiv class=\u0022box col-md-6 bg-primary text-white\u0022\u003E\n      \u003Ch2 class=\u0022h1 text-left\u0022\u003E  Bayesian machine learning\n\u003C\/h2\u003E\n                  \u003Cdiv class=\u0022field-spacing\u0022\u003E\n        \u003Cdiv class=\u0022field__label h3 dash\u0022\u003EECTS :\n          5\n        \u003C\/div\u003E\n      \u003C\/div\u003E\n                  \u003Cdiv class=\u0022field-spacing\u0022\u003E\n        \u003Cdiv class=\u0022field__label h3 dash\u0022\u003ESemester:\n          Semestre 2\n        \u003C\/div\u003E\n      \u003C\/div\u003E\n            \u003Cdiv class=\u0022field-spacing\u0022\u003E\n      \u003Cdiv class=\u0022field__label h3 dash\u0022\u003ED\u00e9tail du volume horaire\u003C\/div\u003E\n                        \u003Cdiv class=\u0022mt-5 text-justify\u0022\u003E\n                          \u003Cspan class=\u0022field__label h4 mt-5 \u0022\u003ELecture:\u003C\/span\u003E\n                                      \u003Cspan\u003E16\u003C\/span\u003E\n                      \u003C\/div\u003E\n                                \u003Cdiv class=\u0022mt-5 text-justify\u0022\u003E\n                          \u003Cspan class=\u0022field__label h4 mt-5 \u0022\u003EPractical study :\u003C\/span\u003E\n                                      \u003Cspan\u003E12\u003C\/span\u003E\n                      \u003C\/div\u003E\n                                                                          \u003C\/div\u003E\n        \u003Cdiv class=\u0022field-spacing\u0022\u003E\n      \u003Cdiv class=\u0022field__label h3 dash\u0022\u003ELangue d\u0026#039;enseignement\u003C\/div\u003E\n                        \u003Cdiv class=\u0022mt-5 text-justify\u0022\u003E\n                                      \u003Cspan\u003EAnglais\u003C\/span\u003E\n                      \u003C\/div\u003E\n                  \u003C\/div\u003E\n        \u003Cdiv class=\u0022field-spacing\u0022\u003E\n      \u003Cdiv class=\u0022field__label h3 dash\u0022\u003EEnseignement \u00e0 distance\u003C\/div\u003E\n                        \u003Cdiv class=\u0022mt-5 text-justify\u0022\u003E\n                                      \u003Cspan\u003Enon\u003C\/span\u003E\n                      \u003C\/div\u003E\n                  \u003C\/div\u003E\n      \u003C\/div\u003E\n    \u003Cdiv class=\u0022box col-md-6\u0022\u003E\n            \u003Cdiv class=\u0022field-spacing\u0022\u003E\n      \u003Cdiv class=\u0022field__label h3 dash\u0022\u003EPr\u00e9requis\u003C\/div\u003E\n                        \u003Cdiv class=\u0022mt-5 text-justify\u0022\u003E\n                                      \u003Cspan\u003E\u003Cul\u003E\n\u003Cli\u003EAn undergraduate course in probability.\u003C\/li\u003E\n\u003Cli\u003EIt is recommended to have followed either the course of P. Latouche and N. Chopin on \u0022Probabilistic graphical models\u0022 or the course of S. Allassoni\u00e8re on \u0022Computational statistics\u0022 during the first semester.\u003C\/li\u003E\n\u003Cli\u003EPracticals will be in Python and R. A basic knowledge of both languages is required, students should be able to read and write simple programs and load libraries.\u003C\/li\u003E\n\u003C\/ul\u003E\u003C\/span\u003E\n                      \u003C\/div\u003E\n                  \u003C\/div\u003E\n        \u003Cdiv class=\u0022field-spacing\u0022\u003E\n      \u003Cdiv class=\u0022field__label h3 dash\u0022\u003EProgramme \/ plan \/ contenus\u003C\/div\u003E\n                        \u003Cdiv class=\u0022mt-5 text-justify\u0022\u003E\n                                      \u003Cspan\u003E\u003Cp\u003E1\/ Decision theory:\u003C\/p\u003E\n\u003Cul\u003E\n\u003Cli\u003EDescribe frameworks for modelling uncertainty and making decisions ;\u003C\/li\u003E\n\u003Cli\u003EIntroduce minimax and Bayes rules.\u003C\/li\u003E\n\u003C\/ul\u003E\n\u003Cp\u003E2\/ Bayesian principles:\u003C\/p\u003E\n\u003Cul\u003E\n\u003Cli\u003EFormalize expected utility, discuss interpretations ;\u003C\/li\u003E\n\u003Cli\u003EShow how most tasks of the MLer fit into the framework (supervised and unsupervised learning, active learning, model choice, etc.)\u003C\/li\u003E\n\u003Cli\u003EPractical session 1.\u003C\/li\u003E\n\u003C\/ul\u003E\n\u003Cp\u003E3\/ Bayesian computation for ML:\u003C\/p\u003E\n\u003Cul\u003E\n\u003Cli\u003EML-specific Monte Carlo approaches: variational principles, advanced MCMC ;\u003C\/li\u003E\n\u003Cli\u003EPractical session 2.\u003C\/li\u003E\n\u003C\/ul\u003E\n\u003Cp\u003E4\/ Bayesian nonparametrics:\u003C\/p\u003E\n\u003Cul\u003E\n\u003Cli\u003EPopular BNP models: Gaussian, Dirichlet, Pitman-Yor, Gibbs-type, Stick-breaking, Indian Buffet processes, etc ;\u003C\/li\u003E\n\u003Cli\u003EPractical session 3.\u003C\/li\u003E\n\u003C\/ul\u003E\n\u003Cp\u003E5\/ Bayesian methods for deep learning:\u003C\/p\u003E\n\u003Cul\u003E\n\u003Cli\u003EBayesian deep neural networks (BNN) ;\u003C\/li\u003E\n\u003Cli\u003EDropout as a Bayesian approximation ;\u003C\/li\u003E\n\u003Cli\u003EBayesian Generative Adversarial Networks ;\u003C\/li\u003E\n\u003Cli\u003EPractical session 4.\u003C\/li\u003E\n\u003C\/ul\u003E\u003C\/span\u003E\n                      \u003C\/div\u003E\n                  \u003C\/div\u003E\n        \u003Cdiv class=\u0022field-spacing\u0022\u003E\n      \u003Cdiv class=\u0022field__label h3 dash\u0022\u003EObjectifs d\u0026#039;apprentissage\u003C\/div\u003E\n                        \u003Cdiv class=\u0022mt-5 text-justify\u0022\u003E\n                                      \u003Cspan\u003E\u003Cp\u003EBy the end of the course, the students should:\u003C\/p\u003E\n\u003Cul\u003E\n\u003Cli\u003Ehave a high-level view of the main approaches to making decisions under uncertainty ;\u003C\/li\u003E\n\u003Cli\u003Ebe able to detect when being Bayesian helps and why ;\u003C\/li\u003E\n\u003Cli\u003Ebe able to design and run a Bayesian ML pipeline for standard supervised or unsupervised learning ;\u003C\/li\u003E\n\u003Cli\u003Ehave a global view of the current limitations of Bayesian approaches and the research landscape ;\u003C\/li\u003E\n\u003Cli\u003Ebe able to understand the abstract of most Bayesian ML papers.\u003C\/li\u003E\n\u003C\/ul\u003E\u003C\/span\u003E\n                      \u003C\/div\u003E\n                  \u003C\/div\u003E\n        \u003Cdiv class=\u0022field-spacing\u0022\u003E\n      \u003Cdiv class=\u0022field__label h3 dash\u0022\u003EOrganisation g\u00e9n\u00e9rale et modalit\u00e9s p\u00e9dagogiques\u003C\/div\u003E\n                        \u003Cdiv class=\u0022mt-5 text-justify\u0022\u003E\n                                      \u003Cspan\u003E\u003Cul\u003E\n\u003Cli\u003E8\u00d73 hours of lectures and practice TPs ;\u003C\/li\u003E\n\u003Cli\u003E4 hours of \u00ab student seminars \u00bb for the evaluation ;\u003C\/li\u003E\n\u003Cli\u003EAll classes and material will be in English. Students may write their final report either in French or English.\u003C\/li\u003E\n\u003Cli\u003EEvaluation is project-based, each project revolving around a research paper.\nStudents form groups (the number of students by project will depend on the number of students in the class). Each group reads and reports on a research paper from a list. We strongly encourage a dash of creativity: students should identify a weak point, shortcoming or limitation of the paper, and try to push in that direction. This can mean extending a proof, implementing another feature, investigating different experiments, etc.\nDeliverables are a small report and a short oral presentation in front of the class, in the form of a student seminar, which will take place during the last lecture.\u003C\/li\u003E\n\u003C\/ul\u003E\u003C\/span\u003E\n                      \u003C\/div\u003E\n                  \u003C\/div\u003E\n                        \u003Cdiv class=\u0022field-spacing\u0022\u003E\n      \u003Cdiv class=\u0022field__label h3 dash\u0022\u003EBibliographie\u003C\/div\u003E\n                        \u003Cdiv class=\u0022mt-5 text-justify\u0022\u003E\n                                      \u003Cspan\u003E\u003Cul\u003E\n\u003Cli\u003EParmigiani, G. and Inoue, L. 2009. Decision theory: principles and approaches. Wiley. - Robert, C. 2007. The Bayesian choice. Springer. - Murphy, K. 2012. Machine learning: a probabilistic perspective. MIT Press. - Ghosal, S., \u0026amp; Van der Vaart, A. W. 2017. Fundamentals of nonparametric Bayesian inference. Cambridge University Press.\u003C\/li\u003E\n\u003C\/ul\u003E\u003C\/span\u003E\n                      \u003C\/div\u003E\n                  \u003C\/div\u003E\n        \u003Cdiv class=\u0022field-spacing\u0022\u003E\n      \u003Cdiv class=\u0022field__label h3 dash\u0022\u003ENature de l\u0026#039;\u00e9valuation\u003C\/div\u003E\n                        \u003Cdiv class=\u0022mt-5 text-justify\u0022\u003E\n                                      \u003Cspan\u003EEvaluation Continue non Int\u00e9grale\u003C\/span\u003E\n                      \u003C\/div\u003E\n                  \u003C\/div\u003E\n      \u003C\/div\u003E\n  \u003C\/div\u003E\n\u003C\/div\u003E\n","settings":null}]