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

This lecture introduces us to the topic of supervised learning. Here the data consists of input-output pairs. Inputs are also often referred to as covariates, predictors and features; while outputs are known as variates, targets and labels. | Linear regression Nando de Freitas UNIVERSITY OF I OXFORD . Outline This lecture introduces us to the topic of supervised learning. Here the data consists of input-output pairs. Inputs are also often referred to as covariates predictors and features while outputs are known as variates targets and labels. The goal of the lecture is for you to Understand the supervised learning setting. Understand linear regression aka least squares Understand how to apply linear regression models to make predictions. Learn to derive the least squares estimate. Linear supervised learning Many real processes can be approximated with linear models. Linear regression often appears as a module of larger systems. Linear problems can be solved analytically. Linear prediction provides an introduction to many of the core concepts of machine .

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