[{"command":"insert","method":null,"selector":".course-modal-ajax-placeholder-965808","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  Introduction to 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\u003ESemestre calendaire :\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\u003ECours magistraux :\u003C\/span\u003E\n                                      \u003Cspan\u003E10\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\u003ETravaux pratiques :\u003C\/span\u003E\n                                      \u003Cspan\u003E8\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\u003ETravaux dirig\u00e9s :\u003C\/span\u003E\n                                      \u003Cspan\u003E6\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 \u0026amp; Motivation\u003C\/p\u003E\n\u003Cp\u003E2. Mathematical and Statistical Foundations\u003C\/p\u003E\n\u003Cp\u003EReview of linear algebra and probability relevant to ML\u003C\/p\u003E\n\u003Cp\u003ELoss functions and optimization (gradient descent intuition)\u003C\/p\u003E\n\u003Cp\u003EMetrics for regression and classification\u003C\/p\u003E\n\u003Cp\u003E3. Data Preparation\u003C\/p\u003E\n\u003Cp\u003EData collection, cleaning, and preprocessing\u003C\/p\u003E\n\u003Cp\u003EFeature scaling, normalization, and encoding\u003C\/p\u003E\n\u003Cp\u003ESplitting data: training, validation, and test sets\u003C\/p\u003E\n\u003Cp\u003EIntroduction to cross-validation\u003C\/p\u003E\n\u003Cp\u003E4. Supervised Learning Basics\u003C\/p\u003E\n\u003Cp\u003ELinear regression and polynomial regression\u003C\/p\u003E\n\u003Cp\u003ELogistic regression for binary classification\u003C\/p\u003E\n\u003Cp\u003Ek-Nearest Neighbors (kNN)\u003C\/p\u003E\n\u003Cp\u003EDecision Trees: basic concept and overfitting control (pruning)\u003C\/p\u003E\n\u003Cp\u003E5. Model Evaluation and Selection\u003C\/p\u003E\n\u003Cp\u003EMetrics: accuracy, precision, recall, F1-score, ROC\/AUC\u003C\/p\u003E\n\u003Cp\u003EConfusion matrix and error analysis\u003C\/p\u003E\n\u003Cp\u003EBias\u2013variance decomposition\u003C\/p\u003E\n\u003Cp\u003ERegularization (Ridge, Lasso)\u003C\/p\u003E\n\u003Cp\u003E6. Unsupervised Learning Overview\u003C\/p\u003E\n\u003Cp\u003EClustering: k-Means, hierarchical clustering\u003C\/p\u003E\n\u003Cp\u003EDimensionality reduction: PCA and visualization techniques\u003C\/p\u003E\n\u003Cp\u003EApplications and limitations\u003C\/p\u003E\n\u003Cp\u003E7. Ensemble and Kernel Methods (Introductory Overview)\u003C\/p\u003E\n\u003Cp\u003EMotivation for ensembles: variance reduction\u003C\/p\u003E\n\u003Cp\u003ERandom Forests (conceptual overview)\u003C\/p\u003E\n\u003Cp\u003ESupport Vector Machines (idea of margin and kernel trick)\u003C\/p\u003E\n\u003Cp\u003E8. Modern Perspectives\u003C\/p\u003E\n\u003Cp\u003EIntroduction to neural networks\u0026nbsp;\u003C\/p\u003E\n\u003Cp\u003EOverview of deep learning and current ML applications\u003C\/p\u003E\n\u003Cp\u003EEthical and societal aspects of machine learning\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}]