tailieunhanh - Lecture Machine learning (2014-2015) - Lecture 3: Maximum likelihood

In this lecture, we formulate the problem of linear prediction using probabilities. We also introduce the maximum likelihood estimate and show that it coincides with the least squares estimate. The goal of the lecture is for you to learn: Gaussian distributions, how to formulate the likelihood for linear regression, computing the maximum likelihood estimates for linear regression, entropy and its relation to loss, probability and learning. | UNIVERSITY OF OXFORD r tút. Ifc-M Maximum Likelihood Nando de Freitas fiwcjdr jwilrtjijS- K3l vrT fi . Outline of the lecture In this lecture we formulate the problem of linear prediction using probabilities. We also introduce the maximum likelihood estimate and show that it coincides with the least squares estimate. The goal of the lecture is for you to learn Gaussian distributions How to formulate the likelihood for linear regression Computing the maximum likelihood estimates for linear regression. Entropy and its relation to loss probability and learning. Univariate Gaussian .

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