Learning objectives
The course offers the theoretical basis for modeling and solving artificial intelligence problems.
The course includes theoretical lessons that introduce the topics of machine learning and the search for solutions from an algorithmic point of view.
With reference to the Dublin Indicators:
Knowledge and understanding
The course introduces concepts relating to the development, application and validation of artificial intelligence algorithms for machine learning and the search for solutions. In particular, supervised and unsupervised machine learning algorithms, randomized search algorithms and swam intelligence are treated.
Ability to apply knowledge and understanding
The theoretical knowledge is introduced in the perspective of both the correct application of state-of-the-art algorithms and interpretation of the results, and in understanding their motivations for the development of new computational solutions.
Autonomy of judgment
A critical approach on the use and understanding of current artificial intelligence algorithms is one of the main objectives of the course. In particular, identify the correct methodology and evaluate its impact on the specific application case.
Communication skills
The discussions on the different methods for solving the proposed problems allow you to improve communication skills through presentation in the form of a seminar.
Learning ability
The autonomous use of external resources and the consultation of scientific literature allows you to develop an autonomous learning ability. The student acquires the ability to adapt to the problem and to apply the most suitable models for the resolution.