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

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. | UNIVERSITY OF OXFORD Logistic regression a simple ANN Nando de Freitas Ĩ. Outline of the lecture 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. How to do logistic regression with the softmax link. McCulloch-Pitts model of a .

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• Machine learning
• Lecture Machine learning
• Machine learning algorithms
• Data analysis methods
• Logistic regression
• Logistic regression likelihood
• Max margin learning
• Multi instance learning
• Reinforcement learning
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• Machine Learning Introduction
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• Bayesian Learning
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