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

Machine Learning for Engineers