- Simple Linear Algorithms, Optimization and Training
- Non linear Solutions and MLP
- Gradient Descent and Backpropagation
- Decision Tree, Random Forest and Ensembles
- Principles and Practice of ML:
- Training, Validation and Testing
- Overfitting and Regularization
- Errors, Performance Metrics and Reliable Error Estimates
- Support Vector Machines and Kernels