Computer Science

This course covers important algorithms and theories for data mining. Data mining refers to theories and techniques for finding useful patterns from massive amounts of data. Data mining has been used in high impact applications including web analysis, recommendation system, fraud detection, cyber security, etc. 

Main topics include finding similar items, mining frequent patterns, link analysis, link prediction, recommendation system, data stream mining, clustering, graph mining, time series prediction, and outlier detection. 

Prerequisite: Students should have an undergraduate-level knowledge on the following topics: Algorithms, Basic probability, Programming, Linear Algebra 

The course will provide some background but will be fast paced. 

Nowadays Cloud Computing is everywhere. Cloud Computing (CC) is not a revolution of Information technology (IT), but It is one of the key evolution steps of IT. It is computing as a utility, which has recently emerged as a commercial reality. The main characteristics of CC are 1) the illusion of infinite computing resources, 2) the ability to pay-as-you-go, and 3) the elimination of an up-front commitment by Cloud users. In other words, CC is a style of computing which can be scaled dynamically, and virtualized resources are provided as a service over the Network. The key idea behind this course is to provide fundamental CC topics taking into account both technology and business considerations. The course is divided into a series of lectures, each of which is accompanied by one or more hands-on exercises. Some of the topics covered are: Fundamental CC terminology and concepts; CC definition an its specific characteristics; Benefits, Challenges and Risks of CC platforms and Services; Roles of CC administrator and owners; SaaS, PaaS, and IaaS delivery models and their combinations; Various Public, Private, and hybrid CC environments; Business Cost models and Service Level Agreements for CC; Case Studies: Google Cloud, Microsoft Cloud, and Amazon Cloud.

Because of the development of music-related AI area and multidisciplinary trends in science and music, the skills of digital music and audio synthesis are gradually needed by industries. The knowledge of digital music involves three areas: music, electrical engineering, and computer science. This course teaches how to program and design digital music, utilizing related programming languages, including chucK (for sound synthesis), Python (for edit and analyzing MIDI data), and Scratch (for auditory-visual interactive projects). 

Course Prerequisite: "Learning Programming for Music" or any other related text-based programming courses. 

This course provides general knowledge in radio frequency applications, especially those which are common in radio communications. The fundamentals are introduced without penetrating the electronics or design details. The different parts are treated as functional blocks defined by their physical properties. This gives a basic understanding of the radio receiver or the cellular phone but also the requirements put on the used circuits. Thus, this is a compulsory course for those who later want to specialize as radio frequency designers.

This course covers various proof techniques and provides practice proving sample propositions using these techniques. Students learn basic discrete mathematics and theoretical computer science topics such as sets and functions, and practice proving propositions related to these topics. The course also covers intermediate discrete mathematics topics, including trees and graphs, and provides practice proving related propositions. Students also learn additional discrete mathematics topics (e.g., counting, probability), and apply proof techniques to prove related propositions. While there is no specific prerequisite course required, students should have basic mathematical knowledge. 

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.