Computer Science

The course covers central ideas within the area of human-computer interaction, as well as theory and methods. The course also covers questions of methods that concern the planning and carrying out of studies in the context of construction/design of a program/system and the evaluation of such. During the course, the following topics are addressed: user-centered design perspectives on human-computer interfaces; design of information systems and interaction; interaction models for different types of computer application; standards and guidelines for dialogue construction; reflection on system development trends; methods and techniques for analysis and evaluation.

The course applies AI techniques to the problems of making devices capable of interacting with the physical world. This includes moving around in the world (mobile robotics), moving things in the world (manipulation robotics), acquiring information by direct sensing of the world (e.g. machine vision) and, importantly, closing the loop by using sensing to control movement. 

 

This course examines the principles, design, administration, and implementation of database management systems. Emphasis is placed on relational database systems. Course topics include: entity-relationship model, relational model, relational algebra and calculus, database design and normalization, database query languages, indexing schemes, integrity, concurrency control, and query processing.

This course is a broad introduction to Human-Computer Interaction (HCI), with an emphasis on techniques, models, theories, and applications for designing, prototyping, and evaluating current and future interactive systems for human use. HCI is an interesting and important area of study, providing the human perspective to computing. Besides technology and innovation, it also touches on issues like ethics and social responsibilities related to technologies in the real world. Selected topics may include multimodal interaction design, ubiquitous/mobile computing, virtual/augmented reality, agents and robots, and HCI applications in various domains such as education, health, urban sustainability, and scientific discoveries.

This course provides an introduction to several subdomains of machine learning and gives an orientation to fundamental methods and algorithms within these domains. The following topics are covered: basic knowledge concerning theories and methods related to the following subdomains; unsupervised and supervised learning, classification, and regression; neural networks, including convolutional neural networks, recurrent neural networks and deep learning; Bayesian learning; reinforcement learning; support vector machines, decision trees, random forests, ensemble methods; hardware and software architectures for machine learning, parallelisation, use of GPUs.

This course provides an overview of information technology and the IT field in general. The course is composed of lectures by professors from school of electronics engineering and computer science at Peking University. Topics include introduction of information technology, computer software, computer architecture, computer network and information systems, computer theory, computational linguistics and machine translation, wireless communication, optical communication, physical electronics and nanotechnology, quantum electronics and laser sciences, microelectronic devices and technology, system on a chip, micro-electromechanical systems, machine perception, and multi-media process.

This course introduces students to the basic principles, methods, and techniques of AI to provide the foundations for more advanced courses in this area. It explores the approaches in the field by referring to the wider historical context in which the AI vision was set and will further motivate the content to be taught by presenting existing and potential applications. Topics include first principles, including the role of first-order logic for knowledge representation, computational reasoning and problem solving systems, the use of search as a capability for exploring alternative solutions, and how AI systems use knowledge to plan and learn from first principles. Students develop intelligent systems by assembling capabilities to concrete computational problems; understand the role of knowledge representation, problem solving, and learning in intelligent-system engineering; and appreciate the role of problem solving, in wide applications that require a basic understanding of human intelligence from a computational perspective.

This course is a study of the general techniques and ideas found in professionally written numerical software, as well as the general concepts students need to know to apply suitable software to computational problems. The course is designed more for potential users of mathematical software than for potential creators of such software. At the completion of the course, students are able to choose an appropriate numerical method to solve a problem, and evaluate the numerical method with respect to potential accuracy, computational efficiency, and memory requirements. Students are also taught to perform the required computations using Matlab or similar systems, and then evaluate the quality of the solution with respect to the accuracy obtained and the sensitivity to model parameter variations. They then estimate whether the quality of the solution is adequate relative to the desired use of the model, and analyze the reasons behind a possible total failure of a method when applied to a concrete problem. This course also covers simple mathematical models from science and numerical analyses of them. Students learn about fundamental numerical methods for the solution of linear and nonlinear equations, linear and nonlinear optimization, eigenvalue problems, initial value problems for ordinary differential equations, partial differential equations, and the fast Fourier transform.

This course is part of the LM degree program and is intended for advanced level students. Enrollment s by consent of the instructor. This course discusses theoretical foundations, computational properties, and use cases for some of the most popular supervised and unsupervised machine learning techniques. In particular, the course addresses tasks such as classification, clustering, and discovery of rules by using modern machine learning methods and libraries.