- Statistical learning theory for classification and regression (PAC model, empirical risk minimization, Vapnik-Chervonenkis theory)
- Linear approaches for classification (perceptron, logistic regression, support vector machines, kernel trick)
- Feedforward neural networks
- Training via stochastic optimization, regularization, validation, and testing
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.