Computer Science

This course explores advanced principles of computer networks based on fundamentals of the topic. The topics are protocol mechanisms, principles of implementation, network algorithms, advanced network architectures, network simulation, network measurement as well as techniques of protocol specification and verification. Protocols mechanisms and techniques of protocols used in network protocols include signaling, separation of control and data channel, soft state and hard state, using of randomization, indirection, multiplexing of resources, localization of services, and network virtualization (overlays, VxLANs, peer-to-peer networks). The identification and study of principles that lead to the implementation of network protocols include system principles, reflections on efficiency, and caveats/ case studies. Network architecture examines “the big picture”. It identifies and studies principles that lead the design of network architectures. The course considers substantial questions rather than specific protocol and implementation tricks, which include internet design principles, lessons learned from the internet, architecture of telephone network, and circuit switching versus packet switching (revisited). Protocols cover network algorithms, self stabilization (examples of routing), Kelly's congestion control framework, and closed loop control on the example of TCP. Simulation, oblivious routing and routing in cryptocurrency networks includes principles of discrete event simulation, analysis of simulation results, packet versus flow models, bounding strategies (e.g., Chernoff bounds), and Gaussian distributions.

Human-Computer Interaction (HCI) is a distinctive branch of computer science dedicated to understanding the relationship between people and computers. It provides a set of techniques that enable software engineers to develop computing applications that better respond to the needs, abilities and interests of customers, clients and end-users. This course provides theoretical grounding, practical knowledge, and hands on experience of key skills needed to design and build better interfaces for computing systems.

This is a cross-course between artificial intelligence (AI) methods and economics. The course will demonstrate to students how artificial intelligence methods can aid economists in obtaining and analyzing various large datasets through numerous economic research examples. With the help of AI technology, people can gain a deeper understanding of the operating laws of complex economic systems, explore potential solutions to real-world economic problems, and predict future economic trends. This course will also utilize economic knowledge to analyze the market competition patterns and development trends of the artificial intelligence industry in China.

This course introduces parallel programming and covers the following main topics: 1) Vector and superscalar processors: architecture and programming model, optimizing compilers (dependency analysis and code generation), array libraries (BLAS), parallel languages (Fortran 90). 2) Shared-memory multi-processors and multicore CPUs: architecture and programming models, optimizing compilers, thread libraries (Pthreads), parallel languages (OpenMP). 3) Distributed-memory multi-processors: architecture and programming model, performance models, message-passing libraries (MPI), parallel languages (HPF). 4) Hybrid parallel programming for clusters of mutlicore CPUs with MPI+OpenMP.

This course introduces data science techniques to harness financial data for making sound financial decisions or answering questions of financial interests. It combines tools used in a variety of fields (finance, economics and statistics). Students will finish the course equipped with a workman’s familiarity with the tools of financial data science, facility with financial data handling and statistical programming, and—hopefully—a good understanding of what decisions you want to make, or what questions you want to ask and how best to do it with econometric tools and financial data.

This course introduces the basic theories, model architectures, algorithms, and implementation of deep learning for computer vision. Students obtain hands-on experience on implementing and training deep neural networks for computer vision tasks. The course covers the following topics: (1) neural network optimization algorithms; (2) backbone network architectures for computer vision, including convolutional neural networks and transformers; (3) network structure design for visual recognition tasks (image classification, object detection, image segmentation), and visual content generation tasks; (4) implementation and training of neural networks for computer vision tasks; (5) advanced topics in computer vision and deep learning. 

This course covers the tools and systems used to implement cloud computing systems, and presents key issues to be addressed, such as virtualization. Students learn cloud system platform technologies and detailed component technologies then configure servers and perform programming on public clouds like Amazon Cloud System (AWS) or Google Cloud System. 

Topics include Cloud computing concepts, Cloud computing models, Cloud computing architecture, Cloud computing platforms, Virtualization, Synchronization, Coordination, Distributed deadlock. 

This course covers machine learning techniques to analyze visual data. Specifically, this course focuses on fundamental machine learning and recent deep learning methods that are widely used in visual data analysis and discusses how these methods are applied to solve various problems with visual data. This course consists of lectures, practices, and projects. 

Topics include Introduction to CV/DL, Convolutional neural networks, Training, optimization, data, Few-shot learning, Object detection and segmentation, RNNS, Domain adaptation, Multimodal learning, Deployment. 

Prerequisite: Basic knowledge of Python 

In this course, students learn the process for how to design, build, test, deploy, maintain, and monitor scalable and robust data products using the Data Product Life Cycle (DPLC). Students gain hands-on experience working with datasets and use cases, collaborating in teams, and applying agile methodologies to deliver data products that meet the needs of real world stakeholders. The course covers the entire DPLC process, including experimentation and productization, with a focus on reliability, fault tolerance, scalability, deployment, and meeting regulatory requirements. The course prepares students for careers in data & digital technology, equipping them with the knowledge and skills required to work in cross-functional teams and navigate complex regulatory requirements.

This course explores mathematical concepts that are useful and frequently used in machine learning. Students examine linear algebra (vector spaces, scalar products, orthogonal vectors, matrices as linear mappings, determinants, eigenvalue and eigenvectors), analysis (differentiation), and probability theory (multidimensional probability distributions, calculations with expected values and variances). The class also discusses some contemporary applications of mathematics in machine learning.