[{"command":"insert","method":null,"selector":".course-modal-ajax-placeholder-968995","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  Advanced Deep Learning\n\u003C\/h2\u003E\n                  \u003Cdiv class=\u0022field-spacing\u0022\u003E\n        \u003Cdiv class=\u0022field__label h3 dash\u0022\u003EECTS :\n          2.5\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 1\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\u003E12\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\u003EDirected 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\u003Cp\u003EPython\u003C\/p\u003E\n\u003Cp\u003EProgramming\u003C\/p\u003E\n\u003Cp\u003EDeep learning course of M1 GENIOMHE-AI or equivalent\u003C\/p\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\u003E\u003Cstrong\u003E4. Programme\/Plan\/Contenus\u003C\/strong\u003E\u003C\/p\u003E\n\u003Cp\u003E- Introduction to deep learning for precision medicine: motivation, applications, and challenges\u003C\/p\u003E\n\u003Cp\u003E- Advanced recurrent architectures (Transformers) and large language models (LLMs)\u003C\/p\u003E\n\u003Cp\u003E- Advanced computer vision architectures (ViT)\u003C\/p\u003E\n\u003Cp\u003E- Generative adversarial networks (GAN)\u003C\/p\u003E\n\u003Cp\u003E- Overview of machine learning paradigms, including multi-task, self-supervised, federated, continual, and meta-learning in the context of precision medicine applications.\u003C\/p\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\u003EThis course explores further the recent developments of AI and deep learning.\u003C\/p\u003E\n\u003Cp\u003EStudents will also have the opportunity to explore advanced model architectures such as transformers and diffusion models, which constitute the foundation of many state-of-the-art systems, including modern conversational agents like ChatGPT and scientific breakthroughs such as AlphaFold. The course will also cover advanced learning paradigms, including multi-task, continual, and meta-learning, and show how these approaches can be applied to real problems in precision medicine.\u003C\/p\u003E\n\u003Cp\u003EStudents will be introduced to advanced features of deep learning frameworks, such as PyTorch, through practical sessions that showcase precision medicine applications.\u003C\/p\u003E\n\u003Cp\u003EThey will further take part in a cutting-edge research project proposed by researchers from the IBISC laboratory.\u003C\/p\u003E\n\u003Cp\u003EBy following this course, students will be able to:\u003C\/p\u003E\n\u003Cp\u003E- understand the key mathematical foundations of deep learning models\u003C\/p\u003E\n\u003Cp\u003E- design, train, and evaluate advanced deep learning architectures for precision medicine applications (and beyond)\u003C\/p\u003E\n\u003Cp\u003E- identify appropriate advanced architectures for specific applications (image, time series, text, etc.)\u003C\/p\u003E\n\u003Cp\u003E- use advanced features of deep learning\u0026nbsp; popular frameworks like PyTorch (TensorFlow, and Keras) to build and train advanced deep learning architectures\u003C\/p\u003E\n\u003Cp\u003E- explain major learning paradigms\u2014including multi-task learning, continual learning, and meta-learning\u2014and apply them effectively in precision medicine contexts\u003C\/p\u003E\n\u003Cp\u003E- participate in a cutting-edge AI and precision medicine research project proposed by researchers from the IBISC laboratory\u003C\/p\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\u003Cp\u003EThis course will be evaluated through a written theory exam and a project report.\u003C\/p\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\u003EComp\u00e9tences\u003C\/div\u003E\n                        \u003Cdiv class=\u0022mt-5 text-justify\u0022\u003E\n                                      \u003Cspan\u003E\u003Cp\u003E\u003Cstrong\u003EComp\u00e9tence 1.1\u003C\/strong\u003E\u003Cbr\u003E\n\u003Cstrong\u003EFormuler des probl\u00e9matiques et des hypoth\u00e8ses de travail permettant la conception et la r\u00e9alisation d\u2019un projet dans son domaine d\u2019expertise et \/ou \u00e0 l\u2019interface de plusieurs domaines en biologie.\u003C\/strong\u003E\u003C\/p\u003E\n\u003Cp\u003E\u003Cstrong\u003EComp\u00e9tence 1.2\u003C\/strong\u003E\u003Cbr\u003E\n\u003Cstrong\u003EConcevoir une d\u00e9marche exp\u00e9rimentale.\u003C\/strong\u003E\u003C\/p\u003E\n\u003Cp\u003E\u003Cstrong\u003EComp\u00e9tence 1.3\u003C\/strong\u003E\u003Cbr\u003E\n\u003Cstrong\u003E\u00c9tablir, r\u00e9aliser et optimiser des protocoles exp\u00e9rimentaux.\u003C\/strong\u003E\u003C\/p\u003E\n\u003Cp\u003E\u003Cstrong\u003EComp\u00e9tence 1.4\u003C\/strong\u003E\u003Cbr\u003E\n\u003Cstrong\u003EProduire et valider des donn\u00e9es scientifiques.\u003C\/strong\u003E\u003C\/p\u003E\n\u003Cp\u003E\u003Cstrong\u003EComp\u00e9tence 2.1\u003C\/strong\u003E\u003Cbr\u003E\n\u003Cstrong\u003EActualiser ses connaissances th\u00e9oriques et technologiques par une veille dans son domaine en relation avec l\u2019\u00e9tat de la recherche\u003C\/strong\u003E\u003C\/p\u003E\n\u003Cp\u003E\u003Cstrong\u003EComp\u00e9tence 2.2\u003C\/strong\u003E\u003Cbr\u003E\n\u003Cstrong\u003ESynth\u00e9tiser et structurer un \u00e9tat de l\u2019art sur une probl\u00e9matique d\u00e9finie, en autonomie, dans un contexte li\u00e9 \u00e0 la recherche scientifique et\/ou recherche \u0026amp; d\u00e9veloppement\u003C\/strong\u003E\u003C\/p\u003E\n\u003Cp\u003E\u003Cstrong\u003EComp\u00e9tence 2.3\u003C\/strong\u003E\u003Cbr\u003E\n\u003Cstrong\u003ESe former \u00e0 de nouveaux outils technologiques.\u003C\/strong\u003E\u003C\/p\u003E\n\u003Cp\u003E\u003Cstrong\u003EComp\u00e9tence 3.1\u003C\/strong\u003E\u003Cbr\u003E\n\u003Cstrong\u003EStructurer et pr\u00e9senter des donn\u00e9es et concepts scientifiques \u00e0 des sp\u00e9cialistes ou \u00e0 des n\u00e9ophytes.\u003C\/strong\u003E\u003C\/p\u003E\n\u003Cp\u003E\u003Cstrong\u003EComp\u00e9tence 3.2\u003C\/strong\u003E\u003Cbr\u003E\n\u003Cstrong\u003EArgumenter les choix m\u00e9thodologiques et les interpr\u00e9tations d\u2019une \u00e9tude.\u003C\/strong\u003E\u003C\/p\u003E\n\u003Cp\u003E\u003Cstrong\u003EComp\u00e9tence 3.3\u003C\/strong\u003E\u003Cbr\u003E\n\u003Cstrong\u003EExtraire et questionner des informations pertinentes d\u2019un document scientifique ou de conf\u00e9rences en anglais.\u003C\/strong\u003E\u003C\/p\u003E\n\u003Cp\u003E\u003Cstrong\u003EComp\u00e9tence 4.1\u003C\/strong\u003E\u003Cbr\u003E\n\u003Cstrong\u003ETravailler en autonomie et collaborer avec les membres d\u2019une \u00e9quipe afin d\u2019atteindre des objectifs communs.\u003C\/strong\u003E\u003C\/p\u003E\n\u003Cp\u003E\u003Cstrong\u003EComp\u00e9tence 4.3\u003C\/strong\u003E\u003Cbr\u003E\n\u003Cstrong\u003EOrganiser et planifier son temps de travail de fa\u00e7on \u00e0 respecter des deadlines.\u003C\/strong\u003E\u003C\/p\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\u003Cp\u003EBengio, Y., Goodfellow, I., \u0026amp; Courville, A. (2017). Deep learning (Vol. 1). Cambridge, MA, USA: MIT Press.\u003C\/p\u003E\n\u003Cp\u003EG\u00e9ron, A. (2025). Hands-On Machine Learning with Scikit-Learn and PyTorch. O\u2019Reilly Media, Inc.\u003C\/p\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}]