Computer Science

This course introduces the following topics: operating-system structure, processes, threads, CPU scheduling, process synchronization, deadlocks, main memory, virtual memory, and file-system interface. Students attend two UNIX tutorials. Prerequisite: a course in computer organization and structure.

This course is designed for beginners with some programming experience; those who have some knowledge of Python but are ready to utilize their skills for data analysis and Machine Learning, or experienced developers looking to transition to Data Science. Enrollees must have taken courses related to or on Python programming, or must have an understanding of Python.

This course offers an introduction to videogame design. Topics include: the process-- from concept to implementation; specification and creation of documentation; mechanics; interfaces; interactive storytelling; controls; balance; 2D game design; 3D game design; design of space and location of resources; controls and navigation; multiplayer game design.

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. At the end of the course students know the basic principles of computer vision and image processing algorithms. Thus, they are able to understand and apply a variety of algorithms and operators aimed at either extracting relevant semantic information from digital images or improving image quality. They also understand the diverse challenges and design choices characterizing the main applications and acquire familiarity with software tools widely adopted in these scenarios. Course topics: Basic definitions related to image processing and computer vision–an overview across major application domains: Image Formation and Acquisition–geometry of image formation, pinhole camera and perspective projection, geometry of stereopsis, using lenses, field of view and depth of field, projective coordinates and perspective projection matrix; Camera calibration: intrinsic and extrinsic parameters, lens distortion, camera calibration based on planar targets and homography estimation (Zhang's algorithm); Image rectification and stereo calibration, basic notions on image sensing, sampling, and quantization; Intensity Transformations–image histogram, linear and non-linear contrast stretching, histogram equalization; Spatial Filtering– linear shift-invariant operators, convolution, and correlation; mean and Gaussian filtering, median filtering, bilateral filtering, non-local means; Image Segmentation–binarization by global thresholding, automatic threshold estimation, spatially adaptive binarization, color-based segmentation; Binary Morphology–dilation and erosion, opening and closing- hit-and-miss; Blob Analysis–distances on the image plane and connectivity, labeling of connected components, basic descriptors: area, perimeter, compactness, circularity, orientation and bounding-box, form factor and related descriptors, Euler number, image moments, invariant moments; Edge Detection–image gradient. smooth derivatives: Prewitt, Sobel, Frei-Chen, non-maxima suppression, Laplacian of Gaussion, canny edge detector; Local Invariant Features–detectors and descriptors, Harris Corners, scale invariant features, SIFT features, efficient feature matching by kd-trees; Object Detection–pattern matching by SSD, SAD, NCC and ZNCC, fast pattern matching, shape-based matching, Hough Transform for analytic shapes, Generalized Hough Transform, object detection by local invariant features, Hough-based voting, least-squares similarity estimation. The theoretical part of the course is complemented with 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.

This is a graduate level course that 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 basic notions of complexity and network sciences and the identification, formulation, modelling, and analysis of new problems that arise in modern computing systems. The course requires basic notions of computer system architecture, computer networks, operating systems, and probability theory as a prerequisite. Modern information systems and services often rely on large numbers of independent interacting components to provide their functions. Under certain conditions, the behavior that results from these interactions can be unexpected and surprising. Complexity Science is an interdisciplinary field for studying global behaviors resulting from many simple local interactions in an effort to characterize and control them. Networks allow us to formalize the structure of interactions. They play a central role in the transmission of information, transportation of goods, spread of diseases, diffusion of innovation, formation of opinions and adoption of new technologies. Network Science is an interdisciplinary field for studying the interconnectedness of modern life by exploring fundamental properties that govern the structure and dynamic evolution of networks. The course discusses topics including: Complex systems: definitions, methodologies; Dynamical systems, Nonlinear dynamics; Chaos, Bifurcations and Feigenbaum constant, Predictability, Randomness and Chaos; Models of complex systems, Cellular automata, Wolfram's classification, Game of life; Autonomous agents, Flocking, Schooling, Synchronization, Formation creation; Cooperation and Competition, Game theory basics, Nash equilibrium; Game theory: Prisoner's Dilemma, Coordination games, Mixed strategy games; Adaptation, Evolution, Genetic algorithms, Evolutionary games; Network Science: Definitions and examples; Graph theory, Basic concepts and definitions; Diameter, Path length, Clustering, Centrality metrics; Structure of real networks, Degree distribution, Power-laws, Popularity; Models of network formation; The Erdos-Renyi random model; Clustered models; Models of network growth, Preferential attachment; Small-world networks, Network navigation; Peer-to-peer systems and overlay networks; Structured overlays, DHTs, Key-based routing, Chord; Distributed network formation: Newscast, Cyclon, T-Man; Processes on networks: Aggregation; Rational dynamics: Cooperation in selfish environments, Homophily, Segregation; Diffusion, Percolation, Tipping points, Peer-effects, Cascades.