Machine Learning and Pattern Recognition

Machine Learning and Pattern Recognition are aimed at understanding the mechanisms behind intelligence, interpreted as the capability of extracting knowledge from past experiences and applying it to predict future outcomes or events.

Mathematical models for Machine Learning are based on algorithms that are capable to learn from the available examples to extract a set of rules, reproducing the sophisticated ability to learn that the human mind has acquired through evolution. Beside the intrinsic theoretical interest, Machine Learning has a large number of applications in different fields, including image, sound and text recognition; life sciences, molecular genetics and medical diagnosis; relational marketing; sensors and internet of things; production systems; identification of frauds and anomalies; network complexity and graph analysis; social media analytics.

The door team is mainly focused on methods for supervised learning, based on continuous and discrete support vector machines, kernels, deep learning, neural networks, classification trees; unsupervised learning models for clustering and association rules identification.

Optimization models and methods

A large number of decision making processes arising within companies and the public administration can be cast in form of optimization models: the decision maker identifies a number of feasible actions and defines a criterion for comparing the alternative decisions, such as the total cost or the total gain.

Optimization methods allow to determine the best choice among the alternative decisions in order to minimize the cost or maximize the gain. Optimization models have been applied in a large number of situations in which a set of scarce resources have to be allocated among different activities in the most effective way. Resources can represent people, production processes, raw materials, components, money.

The door team is mainly active in developing and analyzing approximate algorithms for hard problems in mixed integer programming, combinatorial optimization, linear and convex optimization, stochastic optimization.