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My solutions to programming exercises from Stanford online Machine Learning class.

www.ml-class.org/
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Anomaly Detection and Recommender Systems

Anomaly Detection and Recommender Systems

 
 

K-Means Clustering and PCA

K-Means Clustering and PCA

 
 

Linear Regression

Linear Regression

 
 

Logistic Regression

Logistic Regression

 
 

Multi-class classification and neural networks

Multi-class classification and neural networks

 
 

Neural network learning

Neural network learning

 
 

Regularized linear regression and bias-variance

Regularized linear regression and bias-variance

 
 

Support Vector Machines

Support Vector Machines

 
 

README.md

README.md

 
 

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This repo had been maintained during ML-class 2010. Since the following year courses have the same problems, paying respect to the Honor code, I decided to move solutions into private repository.

More information about this course you could find on official website and in wiki.

For information about Octave, download links, installation instruction, etc. go to GNU/Octave or wiki.

Some math and logic on why to choose epsilon = 0.12 for Weights Initialization could be found here in section 4.2.1 «Theoretical Considerations and a New Normalized Initialization».

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My solutions to programming exercises from Stanford online Machine Learning class.

www.ml-class.org/

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