Sillabo dell'insegnamento

• M - Rappresentazione della conoscenza
AASC - Apprendimento Automatico e Scoperta di Conoscenza
Introduction to machine learning: designing a machine learning system, learning settings and tasks, decision trees, k-nearest-neighbour estimation.
AASC - Apprendimento Automatico e Scoperta di Conoscenza
Bayesian decision theory, maximum likelihood and Bayesian parameter estimation.
AASC - Apprendimento Automatico e Scoperta di Conoscenza
Neural networks: perceptron, multilayer neural networks.
AASC - Apprendimento Automatico e Scoperta di Conoscenza
Clustering: k-means, hierarchical clustering.
AASC - Apprendimento Automatico e Scoperta di Conoscenza
Kernel Machines: kernels, reproducing kernel Hilbert spaces, representer theorem, support vector machines for classification, regression and ranking, kernel construction, kernels for structured data.
AASC - Apprendimento Automatico e Scoperta di Conoscenza
Statistical Learning Theory: PAC learning, consistency, VC dimension, generalization and models comparison. Applications to text categorization and bioinformatics.