machine_learning

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Weighted Least Squares and locally weighted linear regression

by Krishna Sankar on February 5, 2012

weight_function

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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Least Squares in Gaussian Noise – Maximum Likelihood

by Krishna Sankar 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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Closed form solution for linear regression

by Krishna Sankar 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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Stochastic Gradient Descent

by Krishna Sankar on November 15, 2011

Convergence Batch Stochastic Gradient Descent

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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Batch Gradient Descent

by Krishna Sankar on October 29, 2011

Measured and predicted  pageviews per article sep2011 dsplog.com

I happened to stumble on Prof. Andrew Ng’s Machine Learning classes which are available online as part of Stanford Center for Professional Development. The first lecture in the series discuss the topic of fitting parameters [...]

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