[{"command":"insert","method":null,"selector":".course-modal-ajax-placeholder-958558","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  Deep learning\n\u003C\/h2\u003E\n                  \u003Cdiv class=\u0022field-spacing\u0022\u003E\n        \u003Cdiv class=\u0022field__label h3 dash\u0022\u003EECTS :\n          2\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          Annualis\u00e9\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\u003E9\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\u003E9\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\u003EProgramme \/ plan \/ contenus\u003C\/div\u003E\n                        \u003Cdiv class=\u0022mt-5 text-justify\u0022\u003E\n                                      \u003Cspan\u003E\u003Cp\u003E- Introduction to deep learning for precision medicine: Motivation\u003Cbr\u003E\n- Perceptron and multilayer perceptron\u003Cbr\u003E\n- Gradient descent and the backpropagation algorithm\u003Cbr\u003E\n- Computation graph and automatic differentiation (autograd) engine\u003Cbr\u003E\n- Optimization (Adam, RMSprop, etc.)\u003Cbr\u003E\n- Regularization techniques\u003Cbr\u003E\n- Convolutional neural networks (CNN) for (medical) image processing (e.g., CT-scans, etc.)\u003Cbr\u003E\n- Recurrent neural networks (RNN) for (medical) time series (e.g., EEG signals, etc.)\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\u003EDeep learning is at the heart of the recent advances in artificial intelligence. Beyond computer vision and natural language processing, deep learning has made significant advances in precision medicine, enabling breakthroughs such as early disease onset prediction, personalized treatment plans, and drug prescription.\u003C\/p\u003E\n\u003Cp\u003EThis course introduces the foundations of deep learning, from the classic perceptron to modern architectures. It will cover the design, training, and rigorous evaluation of deep learning architectures for various types of data, such as images and time series. Emphasis is placed on practicals related to deep learning for precision medicine applications. Students will participate in cutting-edge AI challenges related to precision medicine using deep learning models.\u003C\/p\u003E\n\u003Cp\u003EIn parallel, students will follow the course \u201cFrameworks for Deep Learning,\u201d where they will have the opportunity to model, design, and implement a deep learning library from scratch, including a computation graph and an automatic differentiation engine that enables the training of deep learning models.\u003C\/p\u003E\n\u003Cp\u003EBy following this course, students will be able to:\u003Cbr\u003E\n- understand the key mathematical foundations of deep learning models\u003Cbr\u003E\n- design, train, and evaluate deep learning architectures for precision medicine applications\u003Cbr\u003E\n- identify appropriate architectures for specific applications (image, time series, text, etc.)\u003Cbr\u003E\n- use popular frameworks like PyTorch (TensorFlow and Keras) to build and train deep learning architectures\u003Cbr\u003E\n- engage in AI challenges in the field of precision medicine\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\u003EC1.1. Formuler 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\/p\u003E\n\u003Cp\u003EC1.2. Concevoir une d\u00e9marche exp\u00e9rimentale.\u003C\/p\u003E\n\u003Cp\u003EC1.3. \u00c9tablir, r\u00e9aliser et optimiser des protocoles exp\u00e9rimentaux.\u003C\/p\u003E\n\u003Cp\u003EC1.4. Produire et valider des donn\u00e9es scientifiques.\u003C\/p\u003E\n\u003Cp\u003EC2.3. Se former \u00e0 de nouveaux outils technologiques.\u003C\/p\u003E\n\u003Cp\u003EC3.1. Structurer et pr\u00e9senter des donn\u00e9es et concepts scientifiques \u00e0 des sp\u00e9cialistes ou \u00e0 des n\u00e9ophytes.\u003C\/p\u003E\n\u003Cp\u003EC3.2. Argumenter les choix m\u00e9thodologiques et les interpr\u00e9tations d\u2019une \u00e9tude.\u003C\/p\u003E\n\u003Cp\u003EC3.3. Extraire et questionner des informations pertinentes d\u2019un document scientifique ou de conf\u00e9rences en anglais.\u003C\/p\u003E\n\u003Cp\u003EC4.1. Travailler en autonomie et collaborer avec les membres d\u2019une \u00e9quipe afin d\u2019atteindre des objectifs communs.\u003C\/p\u003E\n\u003Cp\u003EC4.3. Organiser et planifier son temps de travail de fa\u00e7on \u00e0 respecter des deadlines.\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}]