• 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