Computer Science

This course introduces the basic concepts of information system security and the design principles of classic information system security mechanisms, systematically discussing security threats and corresponding system security protection strategies from two aspects: security analysis technology and security protection mechanism. Information system vulnerabilities and malicious behavior analysis techniques, authentication, information protection and isolation mechanisms, access control and other security protection mechanisms are discussed. Using the latest data processing application software as an example, the course analyzes the use of information system security technology, combining case analysis and course experiments, and analyzes the security mechanism design of mainstream information systems and common security detection and security defense technologies.

This course provides an introduction to the field of quantum computing and information, covering a variety of topics ranging from computation and cryptography to foundations of quantum physics. It explores current research topics and discusses how quantum phenomena give rise to new algorithms for machine learning, quantum computational supremacy, cryptographic schemes with unprecedented security guarantees, and device-independent protocols. Topics include fundamentals of quantum computing; the circuit model; basic quantum algorithms and the concept of quantum computational supremacy; Bell inequalities, non-local games, and the concept of device-independence; and basic quantum protocols for cryptography. As part of the exercises, students run simple quantum programs on an actual quantum computer available through the cloud.

This course covers online and reinforcement learning, concepts that break out of the static realm and move into the perpetual cycle of receiving new information, analyzing it, and executing actions based on the updated estimation of reality. This course considers the agents (computer programs, robots, living beings) that learn based on interactions with real or simulated environments: repeated investment in the stock market, spam filtering, online advertising, online routing, medical treatments, games, and robotics. The course also situates online and reinforcement learning to model a much richer range of problems, such as limited and delayed feedback; and even adversarial problems, where the environment deliberately acts against the algorithm (chess, spam filtering). Mathematical tools for developing and analyzing algorithms for these problems are also studied.

This course covers geometric basics of sensor orientation and object reconstruction, including homogeneous coordinates, projectivity and perspective, modelling of image formation, inner and outer orientation, orientation of uncalibrated and calibrated cameras, spatial resection, least-squares adjustment, orientation of the image pair, relative and absolute orientation, spatial triangulation, multi-view geometry, bundle block adjustment, image digitalization, and radiometric basics.

The course covers elementary programming concepts (arithmetic expressions, for-loops, logical expressions, if-statements, functions and classes) that are closely connected to mathematical/technical problems and examples, as well as mathematical manipulations (setting up matrices, solving linear problems, solving differential equations, finding roots).