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Machine Learning in MatLab/Octave

For Python/Jupyter version of this repository please check homemade-machine-learning project.

This repository contains MatLab/Octave examples of popular machine learning algorithms with code examples and mathematics behind them being explained.

The purpose of this repository was not to implement machine learning algorithms using 3^rd party libraries or Octave/MatLab "one-liners" but rather to practice and to better understand the mathematics behind each algorithm. In most cases the explanations are based on this great machine learning course.

Supervised Learning

In supervised learning we have a set of training data as an input and a set of labels or "correct answers" for each training set as an output. Then we're training our model (machine learning algorithm parameters) to map the input to the output correctly (to do correct prediction). The ultimate purpose is to find such model parameters that will successfully continue correct input→output mapping (predictions) even for new input examples.

Regression

In regression problems we do real value predictions. Basically we try to draw a line/plane/n-dimensional plane along the training examples.

Usage examples: stock price forecast, sales analysis, dependency of any number, etc.