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This repository contains MATLAB / Octave implementations of popular machine learning algorithms, along with explanatory code and mathematical derivations, intended as educational material rather than production code. Implementations of supervised learning algorithms (linear regression, logistic regression, neural nets). The author’s goal is to help users understand how each algorithm works “from scratch,” avoiding black-box library calls. Code written so as to expose and comment on mathematical steps. The repository includes clustering, regression, classification, neural networks, anomaly detection, and other standard ML topics. Does not rely heavily on specialized toolboxes or library shortcuts.
Features
- Implementations of supervised learning algorithms (linear regression, logistic regression, neural nets)
- Unsupervised methods (e.g. k-means clustering)
- Anomaly detection examples
- Code written so as to expose and comment on mathematical steps
- Demo scripts and example datasets
- Does not rely heavily on specialized toolboxes or library shortcuts
Project Samples
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AlgorithmsLicense
MIT LicenseFollow Machine Learning Octave
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