MACHINE LEARNING FOR BIG DATA

Academic Year 2022-2023

Teachers

Giuseppe Serra

Course Year

Unit Credits

Teaching Period

Second Period

Course Type

Characterizing

Prerequisites. Knowledge of programming techniques and basic mathematics.

Teaching Methods. Lectures, exercises, and laboratory.

Verification of Learning. The exam consists of a written test and an oral discussion.

Objectives

Knowledge and understanding

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.

Skills

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.

The course aims to present machine learning algorithms and techniques discussing in deep the main features and applicability criteria. Machine Learning is “field of study that gives computers the ability to learn without being explicitly programmed” (Arthur Samuel, 1950). Recently, thanks to the increasing amount of data digitally available, Machine Learning has become an important field of computer science with several applications in different scientific areas such as medicine, automatic text analysis, autonomous driving systems, and so on. The course aims to enable students to acquire basic knowledge to solve machine learning tasks through a proper formulation of the problem, a critical choice of the learning algorithm and an experimental analysis of the obtained results. The course includes laboratory activities so that students can directly test on real applications concepts learned in class.

Texts

Scientific papers, reports, slides, etc available in the E-Learning portal.

Academic Year 2022-2023