# machine_learning

(3 votes, average: 3.67 out of 5)

## Weighted Least Squares and locally weighted linear regression

by on February 5, 2012

From the post on Closed Form Solution for Linear regression, we computed the parameter vector  which minimizes the square of the error between the predicted value  and the actual output  for all  values in the training set. In that [...]

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(2 votes, average: 5.00 out of 5)

## Least Squares in Gaussian Noise – Maximum Likelihood

by on January 15, 2012

From the previous posts on Linear Regression (using Batch Gradient descent, Stochastic Gradient Descent, Closed form solution), we discussed couple of different ways to estimate the  parameter vector in the least square error sense for [...]

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(1 votes, average: 4.00 out of 5)

## Closed form solution for linear regression

by on December 4, 2011

In the previous post on Batch Gradient Descent and Stochastic Gradient Descent, we looked at two iterative methods for finding the parameter vector  which minimizes the square of the error between the predicted value  and the actual [...]

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by on November 15, 2011

For curve fitting using linear regression, there exists a minor variant of Batch Gradient Descent algorithm, called Stochastic Gradient Descent. In the Batch Gradient Descent, the parameter vector  is updated as, . (loop over all [...]

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(2 votes, average: 5.00 out of 5)