1.1 Knowledge and understanding: During the course, the student learns basic knowledge of the main methods of machine learning. He also learns procedures for evaluating and analyzing the obtained results.
1.2 Applied knowledge and understanding: the laboratory activity allows the student to consolidate the theoretical knowledge, presented during the lectures, through their use in real application cases.
2.1 Autonomous assessments: the student acquires theoretical and practical skills that allows him to develop machine learning algorithms and to analyze critically the obtained results.
2.2 Communication skills: the student learns appropriate terminology and he is able to present the main features of machine learning algorithms tested in the course.
2.3 Learning skills: the course aims to provide students with the basic knowledge needed to understand and solve automatically machine learning problems.