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APPRENTISSAGE AUTOMATIQUE 1 Cadres de lapprentissage automatique Machine Learning
>> YOUR LINK HERE: ___ http://youtube.com/watch?v=6V9vZOofiiQ
PDF: https://mohamedkadhem.com/machine-lea... • We present the fundamental concepts and results of machine and deep learning. In this first lecture, we define precisely the Machine Learning (ML) frameworks and give the first results on performance guarantee of ML algorithms in the case of a finite class of hypothesis. The following lessons extend these results to the case of infinite classes (even of infinite dimension). • 0:00 Introduction • 3:48 Basic learning framework • 19:43 Empirical loss minimization (ELM) • 25:47 PAC-learnability • 28:58 Noisy learning framework • 37:04 Bayes optimal hypothesis • 42:55 Agnostic PAC-learnability • 46:30 General learning framework • 57:49 Empirical loss minimization (ELM) • 1:05:26 Learnability versus uniform-convergence • 1:09:51 Finite hypothesis class • 1:12:06 Bibliography • #machinelearning #learningmachine #learninginmachinelearning #machinelearningalgorithms #mlalgorithms #empiricalriskminimization #supervisedmachinelearning #machinelearningwhatis #probablyapproximatelycorrect #mathematicsformachinelearning #datascienceandmachinelearning #introductiontomachinelearning #probabilisticmachinelearning #understandingmachinelearning #basicmachinelearning #statisticalmachinelearning #learningalgorithm #paclearnability #uniformconvergenceproperty
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