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My solutions to programming exercises from Stanford online Machine Learning class.
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Anomaly Detection and Recommender Systems
K-Means Clustering and PCA
Linear Regression
Logistic Regression
Multi-class classification and neural networks
Neural network learning
Regularized linear regression and bias-variance
Support Vector Machines

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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