- nearest neighbor and tree-based methods for supervised learning
- clustering methods (linkage-based, k-means, spectral clustering)
- dimensionality reduction methods (principal component analysis, compressed sensing)
- generative models (discriminant analysis, generative adversarial networks)
- methods in reinforcement learning
Recommended are basic knowledge of:
- Mathematical optimization
- Numerical analysis
- Probability theory
Contributions are welcome! Please follow these steps:
- Fork the repository
- Create a new branch for your feature or bug fix
- Submit a pull request with a clear description of the changes
This project is licensed under the GNU General Public License (GPL). For more details, refer to the GNU License.