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Machine learning algorithms allow us to induce rules based on empirical training data. The course will establish statistical and algebraic foundations essential to kernel-based learning approach, for vectorial and nonvectorial data analysis. More specifically, it covers: (i) aptive techniques for feature selection and dimension reduction; (ii) unsupervised cluster discovery: K-means, SOM, kernel K-means, kernel SOM, and hierarchical clustering; and (iii) upervised learning algorithms: Fisher discriminate analysis (FDA), kernel ridge regressor, upport vector machines, and ridge-SVM.