[{"command":"insert","method":null,"selector":".course-modal-ajax-placeholder-950881","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  [AI] Machine Learning Basics 1\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\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\u003E- (T1A, Applied Stats): Maximum Likelihood Estimate and related notions\u003Cbr\u003E\n- (T1B, Maths for DS): Basic Linear Algebra, well mastered (tensor product, diagonalization)\u003Cbr\u003E\n- python, numpy.\u003Cbr\u003E\nIdeally, too:\u003Cbr\u003E\n- (OPT9, Hands On ML): it\u2019s a good complement to this class, very good to master sklearn. Here we\u2019ll look inside the algos of sklearn or others.\u003Cbr\u003E\n+ Check out previous years exam to see the level of math-mastery that is expected (of course some of it is covered in this course, but you need good basics).\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 is algorithms-oriented, i.e. we will sketch the great principles of ML at first, and then focus on how algorithms work in practice, including all necessary mathematical aspects.\u003Cbr\u003E\nThey are the basic building blocks of more advanced algorithms.\u003Cbr\u003E\n1 Gradient Descent, Linear Regression from scratch\u003Cbr\u003E\n2 Classification with a single layer Perceptron, from scratch. Geometrical interpretation, a word on SGD\/mini-batch learning. Discussion on the choice of the loss function, or activation functions. OVR multi-class scheme (quickly)\u003Cbr\u003E\n3 overfitting, train\/validation\/test split, K-fold CV, regularization: in general, L2, L1.\u003Cbr\u003E\n4 MAP, Bayesian interpretation of Ridge Regression or Lasso one.\u003Cbr\u003E\n5 feature maps (\u0022Kernel trick\u0022), PCA (from scratch, seen as variance maximization), PCA as pre-processing (dimensional reduction)\u003Cbr\u003E\n6 Kernels, Kernelized perceptron, SVM in the separable case (some details omitted). Exercise: Lasso regularization from scratch (pseudo-gradients quickly introduced)\u003C\/p\u003E\n\u003Cp\u003ERecommation of reading:\u0026nbsp;\u003Cbr\u003E\nPattern Recognition and Machine Learning, Christopher Bishop, 2006 (available online for free)\u003Cbr\u003E\nHands On Machine Learning with Scikit Learn and TensorFlow, Aur\u00e9lien G\u00e9ron\u0026nbsp;\u003Cbr\u003E\n-\u0026gt; also in French: Introduction au Machine Learning, Aur\u00e9lien G\u00e9ron\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}]