[{"command":"insert","method":null,"selector":".course-modal-ajax-placeholder-967510","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  Machine learning\n\u003C\/h2\u003E\n                  \u003Cdiv class=\u0022field-spacing\u0022\u003E\n        \u003Cdiv class=\u0022field__label h3 dash\u0022\u003EECTS :\n          3\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\u003EPractical study :\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\u003EProgramme \/ plan \/ contenus\u003C\/div\u003E\n                        \u003Cdiv class=\u0022mt-5 text-justify\u0022\u003E\n                                      \u003Cspan\u003E\u003Cp\u003E1. Introduction to Machine Learning\u003C\/p\u003E\n\u003Cp\u003EOverview of machine learning paradigms: supervised vs. unsupervised\u003C\/p\u003E\n\u003Cp\u003EKey concepts: overfitting, generalization, bias\u2013variance tradeoff\u003C\/p\u003E\n\u003Cp\u003E2. Data Preparation \u0026amp; Representation\u003C\/p\u003E\n\u003Cp\u003EData preprocessing: normalization, feature scaling, encoding categorical variables\u003C\/p\u003E\n\u003Cp\u003EFeature engineering and dimensionality reduction (PCA overview)\u003C\/p\u003E\n\u003Cp\u003ETrain\/validation\/test split and cross-validation\u003C\/p\u003E\n\u003Cp\u003E3. Linear Models\u003C\/p\u003E\n\u003Cp\u003ELinear regression, logistic regression\u003C\/p\u003E\n\u003Cp\u003ERegularization: Ridge, Lasso, Elastic Net\u003C\/p\u003E\n\u003Cp\u003EModel evaluation metrics (classification \u0026amp; regression)\u003C\/p\u003E\n\u003Cp\u003E4. Kernel Methods\u003C\/p\u003E\n\u003Cp\u003EConcept of feature space and kernel trick\u003C\/p\u003E\n\u003Cp\u003ECommon kernels (linear, polynomial, RBF, sigmoid)\u003C\/p\u003E\n\u003Cp\u003EKernelized algorithms: Support Vector Machines, Kernel Ridge Regression\u003C\/p\u003E\n\u003Cp\u003E5. Tree-Based Methods\u003C\/p\u003E\n\u003Cp\u003EDecision Trees: impurity measures, pruning, interpretability\u003C\/p\u003E\n\u003Cp\u003EEnsemble tree methods: Random Forests, Gradient Boosted Trees, XGBoost\u003C\/p\u003E\n\u003Cp\u003EFeature importance and model explainability\u003C\/p\u003E\n\u003Cp\u003E6. Ensemble and Hybrid Methods\u003C\/p\u003E\n\u003Cp\u003EBagging, boosting, stacking\u003C\/p\u003E\n\u003Cp\u003EComparison between linear, kernel, and tree-based methods\u003C\/p\u003E\n\u003Cp\u003E7. Neural Networks (Intro)\u003C\/p\u003E\n\u003Cp\u003EPerceptron and multilayer perceptron (MLP)\u003C\/p\u003E\n\u003Cp\u003EBackpropagation and gradient-based optimization\u003C\/p\u003E\n\u003Cp\u003EComparison with kernel and tree-based methods\u003C\/p\u003E\n\u003Cp\u003E8. Model Selection \u0026amp; Evaluation\u003C\/p\u003E\n\u003Cp\u003ECross-validation and hyperparameter tuning\u003C\/p\u003E\n\u003Cp\u003ERegularization strategies and early stopping\u003C\/p\u003E\n\u003Cp\u003EBias\u2013variance analysis and learning curves\u003C\/p\u003E\n\u003Cp\u003E9. Practical Sessions\u003C\/p\u003E\n\u003Cp\u003EImplementation with Python\/scikit-learn\u003C\/p\u003E\n\u003Cp\u003EModel training, tuning, and evaluation on benchmark datasets\u003C\/p\u003E\n\u003Cp\u003EMini-project: compare linear, kernel, and tree-based methods\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}]