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

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This lecture describes the construction of binary classifiers using a technique called Logistic Regression. The objective is for you to learn: How to apply logistic regression to discriminate between two classes; how to formulate the logistic regression likelihood; how to derive the gradient and Hessian of logistic regression; how to incorporate the gradient vector and Hessian matrix into Newton’s optimization algorithm so as to come up with an algorithm for logistic regression, which we call IRLS.

TÀI LIỆU LIÊN QUAN

Lecture Machine learning (2014-2015) - Lecture 10: Max-margin learning

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
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