Michael Villamizar
Enseignement & Phd
Cours
Machine Learning for Engineers
* Fundamentals 4h
- Notion of learning (classification vs. regression vs. density modeling vs. reinforcement learning)
- Probability theory (formalization, densities, density models)
- Standard statistical tools
- Cross validation and performance evaluation
- Signal processing (Fourier, edges, etc.)
- Optimization (gradient, newton, stochastic gra
- Notion of learning (classification vs. regression vs. density modeling vs. reinforcement learning)
- Probability theory (formalization, densities, density models)
- Standard statistical tools
- Cross validation and performance evaluation
- Signal processing (Fourier, edges, etc.)
- Optimization (gradient, newton, stochastic gra