Machine Learning - Μηχανική Μάθηση (M124)
Σταύρος Περαντώνης
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).
LessParametric 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).
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).
