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Introduction to Artificial Intelligence

The course will introduce the student to the classical topics and techniques of artificial intelligence and knowledge representation, based on logic and search. 

Introduction to Machine Learning (part B)

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. 

Optimization Algorithms

Introduction to operative research, linear programming, duality, sensitivity analysis, and non-linear programming.

Sistemi Dinamici per l’Intelligenza Artificiale

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. 

Calculus I

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.


Calculus II

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.


Data Analytics

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.

Introduction to Machine Learning (part A)

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.

Introduction to Physics

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.


Numerical Analysis

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.

Probability Theory

The course will introduce students to the basic notions of probability theory for discrete and continuous distributions, both univariate and multivariate. 


Statistical Inference

Statistical inference: sampling and sampling distributions, estimation, hypothesis testing, and analysis of variance.