This repo contains exercises covering the main practical implementation of machine learning
- linear regression
- linear regression with polynomial features
- logistic regression
- neural networks
- support vector machine (with gaussian kernel)
- K-means clustering algorithm (K = choice of clusters)
- anomaly detection algorithm: Gaussian and multiVariateGaussian (with threshold epsilon)
- Principal component analysis
- Collaborative filtering
- Matlab or GNU octave
This was my first challenge in Matlab / GNU Octave. The goal was to hae an understanding of pratical applications of some machine learning algorithm. I think I went beyond basic understanding as I did all the necessary mathematical demonstration (available on stats.stackexchange.com).
Install Ocatve or Matlab if you don't have it already (using brew on OSX or directly from the websites: http://www.gnu.org/software/octave/download.html)