Computer Science

This course is part of the Laurea Magistrale program. The course is intended for advanced level students only. Enrollment is by consent of the instructor. The course focuses on the basic algorithms, tools, and systems for the management, processing, and analysis of digital images. Special attention is placed on the design and development of simple systems oriented to real-world computer vision applications such as those requiring segmentation and classification of objects in digital images. The course discusses topics including basic definitions related to image processing and computer vision, image formation and acquisition, intensity transformations, spatial filtering, image segmentation, binary morphology, blob analysis, edge detection, local invariant features, and object detection. The theoretical part of the course is complemented by assisted hands-on lab sessions based on Python and the OpenCV library. Lab sessions cover selected topics such as intensity transformations, spatial filtering, camera calibration, motion estimation, and local invariant features. Students are provided with the software tools, image/video archives, and support that enable practical implementation and testing of most of the topics discussed in class, in order to provide in-depth analysis of the course subject matter.

This course offers an introduction to the basic concepts of programming and to solving mathematical and statistical problems.

This subject examines the theoretical and practical tools required to understand, construct, validate and apply models of standard electrical and electronic devices. In particular, it looks at the theoretical and practical development of models for devices such as resistors, capacitors, inductors, transformers, motors, batteries, diodes, transistors, and transmission lines. 

This course provides a study of the basic techniques for building intelligent computer systems and how Artificial Intelligence is applied to problems. It covers theory, algorithms, and their applications. The course is divided into four parts. The first part of the course includes an introduction to AI, history of AI, problem solving and search. The second part covers machine learning, linear models, decision trees, and neural networks. The third part studies decision marking and includes topics such as, logical agents, quantifying uncertainty, Bayesian networks, Markov decision process, and reinforcement learning. The final part of the course examines natural language processing, computer vision, and robotics.

This course examines computer music for students with basic programming abilities. It covers fundamental audio analysis and synthesis, and algorithmic music with machine learning.