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(M124) -  Σταύρος Περαντώνης

Descripción del Curso

Parametric models, linear regression, least squares, overfitting, bias-variance trade off, ridge regression, maximum likelihood and maximum a-posteriori probability estimation, cross-validation. Bayesian classification and regression. Linear and non-linear classifiers and regressors (perceptrons, multi-layered perceptrons, radial basis functions, support vector machines). Introduction to deep learning. Context-dependent classification models (Markov chains, Viterbi algorithm, hidden Markov models). Introduction to clustering, k-means algorithm. Pattern matching techniques (Bellman principle, Levenshtein distance). 

Creation Date

martes, 9 de octubre de 2018