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

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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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Lecture Machine learning (2014-2015) - Lecture 12: Reinforcement learning

Lecture Machine learning (2014-2015) - Lecture 1: Introduction

Lecture Machine learning (2014-2015) - Lecture 02: Linear prediction

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

Lecture Machine learning (2014-2015) - Lecture 4: Nonlinear ridge regression risk, regularization, and cross-validation

Lecture Machine learning (2014-2015) - Lecture 5: Optimisation

Lecture Machine learning (2014-2015) - Lecture 6: Logistic regression: a simple ANN

Lecture Machine learning (2014-2015) - Lecture 7: Backpropagation: A modular approach (Torch NN)

Lecture Machine learning (2014-2015) - Lecture 8: Neural networks