Objective: introducing the student to state of the art methods for the numerical simulation of partial differential equation.
Objective: Introduce the students to the machine learning fundamentals, to the main techniques on supervised learning, and to the principal application domains. Present evolutionary calculation. The course explains how to design, develop and evaluate simple ML-based end-to-end systems and, at the same time, how to describe their operations.
Objective: introduce the student to the principles of learning from data based on statistics, and to the scientific treatment of data to obtain new and reproducible knowledge. Some of the main supervised and unsupervised statistical learning techniques are presented.
The course introduces the main concepts in information theory and algorithmic information theory.
Introducing to main concepts in quantum mechanics and quantum computing
The course introduces the main concepts and effects of quantum correlation on information theory, computation and machine learning.