Programming For Data Science – Siena

Argomenti del programma: Introduction to learning principles, learning protocols: supervised, unsupervised, semi-supervised, active learning; multitask learning, biological and artificial neurons, neural network architectures, learning principles, biological and artificial neurons, neural network architectures, linear machines, normal equations, cases of well- and ill-position, ridge regression, polynomial regression and Vandermonde…

Argomenti del programma: Programming for data science 1.1 Python programming Introduction to the Python programming language: basic syntax; data structures; object-oriented programming; libraries and modules. 1.2 Libraries for data analysis The scipy ecosystem; numpy arrays; pandas data structures; plotting with Matplotlib; linear algebra using numpy; examples of scientific computing with scipy.