Computer Science

This course develops critical thinking and precise research paper writing skills related to the era and field of visual generative models. 

Topics include generating images, videos, 3D and 4D NeRFs, depth-driven pose-preserved image generation for any objects, 4D reconstruction from a single video, classifier protected sampling for guarding training data during diffusion, sparse surface reconstruction using local geometry priors, mixture of efficient diffusion experts through automatic interval and sub-network selection, and exact volumetric ellipsoid rendering for real-time view synthesis. 

Each week consists of a lecture, discussion, and writing and critiquing reviews in a collaborative setting. Writing topics include understanding style, actions, characters, cohesion and coherence, emphasis, motivation, global coherence, concision, shape and ethics

The course examines various topics in computer game design. The course begins with an introduction to game history and design; user interface, devices, and effect for game; and an industry visit. It then covers 2D and 3D game, platform and team, and software organization. Topics include: types of game, game platforms, design of game, 3D model and kinematics, rendering techniques, collision detection, project management, AI, UI, sound effects, and networking.

This course primarily targets Masters students but also ambitious Bachelor students who want to get the opportunity to broaden their knowledge of specific wireless communication technologies. After completing this course, students will have deep knowledge about wireless technologies from the IEEE 802 protocol family (e.g., WiFi, Bluetooth and ZigBee), technologies for adhoc/mesh networks and classical cellular networks. Additionally, during the labs, students will have the opportunity to study selected technologies or technology-oriented problems in hands-on exercises.

Programming and synchronizing concurrent processes that access shared resources: non-sequential programs and processes in their various forms; non-determinism, determination; synchronization mechanisms: locks, monitors, guards, events, semaphores; non-sequential program execution and object orientation; process control, selection strategies, priorities, dealing with and avoiding deadlocks; co-routines, implementation, multiprocessor systems; interaction via messages; programming and synchronizing concurrent processes that interact via message exchange; remote calling techniques; client-server, peer-to-peer; parallel computing on the network; coordination languages; processing on the server and on the client, mobility; middleware, structured communication, static and dynamic interfaces; event-based and stream-based processing; security of applications on the network; outlook on non-functional properties (time, memory, quality of service).

This course addresses the political and cultural dimensions of artificial intelligence and network technologies in a range of global contexts. Through an interdisciplinary approach that incorporates the fields of media and communication studies, critical theory, and the philosophy of technology, the course examines the technical and conceptual elements of machine learning, digital automation, and online communication in order to develop an understanding of their social impacts and historical consequences. Topics include: the structure of neural networks and online protocols; the rise of cybernetics as a political logic and technical form; the history of digital logistics and automation; ethical issues surrounding the widespread installation of these technologies across different sectors of society. 

A supervised program of study approved by the Head of School.

This course offers a study of basic Machine Learning techniques, when to use Machine Learning on real problems, how to determine which technique is appropriate for each problem, and to apply the techniques in a practical way to real problems. Topics include: classification and prediction techniques; non-supervised techniques; reinforcement-based techniques; relational learning; methodological aspects.

The course brielfy introduces ethics and the history of computing and the Internet. It focuses on a number of areas in which computers and information technology impact society, including work, the environment, privacy, freedom of speech, and intellectual property. 

The course introduces computational thinking as applied to problems in science, with special emphasis on their implementation with Python/Python Notebook. A selection of examples illustrate (a) fundamentals of algorithm design in computer programming (b) solution interpretation, as well as (c) analysis of the computational solutions and data visualization using state-of-the-art tools in Python. These cover different types of approaches typically used in scientific computational thinking, including deterministic, probabilistic and approximation methods. The course highlights scientific computational issues such as accuracy and convergence of numerical results.

This course focuses on how operating systems function as expanded machines, their components, major entities (processes, memory, files, etc.), and relation to the software and hardware of the computer. It explores how to use a program's operating system, the difference between processes and threads, including the main scheduling policies used, how to develop applications with multiple processes or threads using communication and synchronization mechanisms between them, as well as concurrent programming mechanisms.