PROGRAMMA DEL CORSO

Apprendimento automatico; Apprendimento da esempi; Apprendimento di modelli probabilistici; Introduction to probability; Introduction to machine learning; Linear Algebra; Vector Calculus; Optimization; Maximum Likelihood Estimation ; Classification; Covariance Matrix; Principal Component Analysis; Supervised Learning with SKlearn; Clustering; Bayesian Learning; Bayesian Inference; Gaussian Mixture Models; Bayesian Networks with OpenMarkov; Neural Networks; Automatic Differentiation; RBF Neural Networks; Recurrent Neural Networks.

TESTI ADOTTATI E CONSIGLIATI

“Pattern Recognition and Machine Learning”, Chris Bishop, Springer, 2006, 2011

“Deep Learning”, Ian Goodfellow, Yoshua Bengio,…, MIT Press nov 2016

“Hands-On Machine Learning with Scikit-Learn and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems”, A.Geron, O’Reilly