The course will introduce the student to basic algorithms and data structures and to the analysis of algorithmic complexity.
The course will introduce the student to the basics of information theory, the concept of entropy, and the techniques and notions of statistical physics.
The course will introduce the student to the classical topics and techniques of artificial intelligence and knowledge representation, based on logic and search.
The course starts where the first part (module A) ended, introducing additional machine learning techniques and methods, focussing more on the probabilistic and statistical learning perspective.
Introduction to operative research, linear programming, duality, sensitivity analysis, and non-linear programming.
The course will introduce discrete and continuous dynamical systems, including mathematical approaches to investigate their dynamics and their applications to artificial intelligence and complex systems.
The course will introduce programming techniques to fully use modern computer architectures, which have multiple computational units.
This course aims to illustrate the foundations and fundamentals of differential and integral calculus for functions of one variable. The main arguments will be: set of numbers, axioms of real numbers, limits of successions, limits of functions, continuous functions, differential calculus for real functions in one variable, integral calculus for real functions in one variable.
This course aims to illustrate the fundamentals of differential and integral calculus for functions of several variables, of the theory of numerical and functions series, of ordinary differential equations, as well as to introduce students to modeling and solving simple problems of practical interest which exploit these mathematical tools.
The coruse will introduce the foundations of computability theory, complexity classes and intractable problems, and the basics of formal logic.
The goal of the course is to introduce the students to programming.
The course will focis more on technques of data analtytics, including data cleaning and data analysis and visualization.
Modern artificial intelligence techniques require the management of large quantities of data, which are stored in traditional relational databases, nosql databases, or in other formats. The aim of the course is to introduce the student to the management and analysis of data.
Due to the recent application of artificial intelligence in numerous aspects of everyday life, this course will discuss the ethical and legal implications of these applications.
The course introduces the foundations of information theory and statistical physics
The course introduces concepts for the computational and analytical study of complex systems.
The course will introduce the student to the concepts and methods of machine learning, both supervised and unsupervised learning. The course will describe the fundamentals of model building and validation, introducing several learning methods.
The course will introduce some basic concepts of physics (physical quantities, units of measurement, formulation of models and their experimental verification), as well as the physical laws and methods necessary to solve simple problems of Newtonian mechanics, electromagnetism, and thermodynamics.
The aim of the course is to give the practical basis of programming.
The course introduces to basic aspects of linear algebra: vector, unitary and euclidean spaces, and applications (linear, orthogonal and unitary).
The course will continue the mathematical formation to the students, introducing the notions and methods necessary to understand the modern artificial intelligence techniques.
The couse introduces the student to numerical analysis, i.e., to the development and study of numerical methods use to solve problems from mathematical analysis.
The course will introduce students to the basic notions of probability theory for discrete and continuous distributions, both univariate and multivariate.
Statistical inference: sampling and sampling distributions, estimation, hypothesis testing, and analysis of variance.