Olivier Canévet
EPFL AVP-PGE EDEE-ENS
ELB 112 (Bâtiment ELB)
Station 11
CH-1015 Lausanne
Local:
ELB 112
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EDEE-ENS
Enseignement & Phd
Cours
Fundamentals in statistical pattern recognition
1. Introduction
** Data representation
** Supervised/unsupervised models (from regression and classification to probability distribution modelling)
** Overview: Linear models, Principal Component Analysis (PCA), Linear Discriminant Analysis (LDA), Multi-Layered Perceptron (MLP), Support Vector Machines (SVM), k-Means, Gaussian Mixture Models (GMM) Hidden Markov Models (HMM), Ses
** Data representation
** Supervised/unsupervised models (from regression and classification to probability distribution modelling)
** Overview: Linear models, Principal Component Analysis (PCA), Linear Discriminant Analysis (LDA), Multi-Layered Perceptron (MLP), Support Vector Machines (SVM), k-Means, Gaussian Mixture Models (GMM) Hidden Markov Models (HMM), Ses
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