Learning objectives
The course offers the basis for modeling and implementing algorithms and systems for solving artificial intelligence problems.
The course includes theoretical lessons that introduce some selected topics relating to machine learning and automatic reasoning. In addition, the course includes laboratory lessons to allow students to carry out laboratory experiences related to the topics covered.
With reference to the Dublin indicators:
Knowledge and understanding
The course introduces the concepts related 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, search algorithms and sequence analysis algorithms are covered.
Ability to apply knowledge and understanding
The theoretical and practical knowledge presented is introduced in perspective both of the correct application of state-of-the-art algorithms and interpretation of the results obtained, and in understanding their motivations for the development of new computational solutions.
Judgment autonomy
A critical approach on the use and understanding of current tools for solving artificial intelligence problems is among 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 to solve the proposed problems allow to improve communication skills through the development of a project on topics established by the teacher and which is tackled individually or in groups. The results of the project are then presented to the teacher.
Learning ability
The independent use of external resources and the consultation of existing scientific literature and practical tools allows for the development of independent learning skills. The student acquires the ability to adapt to the problem and to apply the most suitable models for the resolution.