Computer Science

Search engine is one of the most important information access tools for network users, search engine design and implementation process, the integrated use of today's Internet applications in the field of the highest level of research results. Students through the study of this course, not only can master and Internet applications closely related to the network information retrieval, network data mining and other aspects of knowledge, but also help to cultivate its comprehensive use of knowledge to solve problems. The teaching objectives of this course include: to understand the basic principles of search engines, product design ideas and commercial operation mode; to master the characteristics of the Internet data environment and its impact on the design and implementation process of the search engine; to learn and master the system design of large-scale commercial search engines and their core algorithms. The course combines classroom lectures with hands-on practice, so that students have a considerable theoretical foundation of search engine and practical ability. This will enable students to acquire knowledge with both theoretical depth and practical integration with the latest search engine applications.

The course provides an overview of the relationships between computing systems and human beings, from a technological perspective. The first weeks introduce the main theoretical and technical concepts of human-computer interaction (HCI), such as cognitive aspects of visual design, interaction design, persuasion, and user experience. The students analyze the risks and possibilities associated to computing interfaces, wearable technologies, and data visualization. The second part of the course focuses on AI and algorithms, with a broad introduction to the main techniques and challenges involved, e.g., machine learning and data science. In this part as well, once equipped with the basic conceptual tools, students focus on the ethical challenges of modern AI systems, with a discussion on the concepts of accountability and trust?

Reinforcement learning (RL) refers to a collection of machine learning techniques which solve sequential decision making problems using a process of trial-and-error. It is a core area of research in artificial intelligence and machine learning, and provides one of the most powerful approaches to solving decision problems. This course covers foundational models and algorithms used in RL, as well as advanced topics such as scalable function approximation using neural network representations and concurrent interactive learning of multiple RL agents.

Humans are a vital component of secure and private systems, they are also one of the most expensive components and the most challenging to reason about. In this course, students learn about how to create systems that are usable while still fulfilling their primary security or privacy mission. Students also learn about research topics such as designing user studies to critically evaluate interfaces and reading academic papers to create an academically-informed view of the topic.

We will cover the basic concepts in modern cryptography. The contents include one-way functions, encryption, pseudorandomness, digital signature, interactive protocols, zero-knowledge proofs, multiparty computation, homomorphic encryption, and program obfuscation.

In this research course, students chose from a range of research topics in various academic fields and receive one-on-one training from an experienced mentor who helps them refine research ideas, formulate questions, define methods of data collection, execute a plan, and present findings. Students review background information for their project, summarize its key outcomes, write a clear and concise research paper or report, and present results orally.

This course introduces the fundamental concept of data structures and the importance of data structures in developing and implementing efficient algorithms. The topics include various data structures such as arrays, linked lists, stacks, queues, strings, graphs, trees, and hash tables. Relevant algorithms will be analyzed to assess the strengths and weaknesses of data structures. The lectures and assignments will primarily be done in Python.

Prerequisite: CSI2102 or an equivalent level of fluency in an objected-oriented programming language.

The course familiarizes students with the issues involved in designing, implementing, and applying parallel programming systems. Initial motivation is provided by consideration of a number of typical high performance applications and parallel architectures. This highlights the role of parallel software systems as a means of bridging the gap between these and allows abstraction of the issues which must be addressed by any such system (partitioning, communication, agglomeration, scheduling). It explores the ways in which these challenges have been addressed by a range of systems, including both de facto standards and more adventurous research projects.

This is an introduction to quantum computer science, intended primarily for computer scientists, physicists, electrical engineers, and mathematicians. It introduces a large number of ideas with an emphasis on building familiarity with the main concepts, and some general knowledge of terminology and methods. Mathematical methods are employed in a practical way, on a "need-to-know" basis. 

This class discusses the basic concepts and methods of information resource management, including capturing, representing, organizing, storing, processing and exploiting information. In particular, the introductory session will provide an overview of the definition and general types of information, the new forms of information in the era of social media, and the definition of information source. Web search engines, as one of the most important channels to obtain information in our daily life, will be discussed. Then, the class will cover the process of capturing, encoding, and initial processing of different information in digital media, followed by the essence of information management and extraction technologies, such as data warehouse, XML, and the Semantic Web. However, while more and more available information accelerates the development of new knowledge, issues pertaining to information security become evident too. Hence, this module also briefly explains the concepts of confidentiality, integrity and availability, as well as the mechanisms that provide security in various information systems and applications. Next, this module focuses on the applications of information resource management technologies in enterprises and in Web 2.0-baed e-commerce. First, the information architecture, strategies and services in enterprises w1 be introduced. Several cases on how information can be a strategic resource for companies will be studied. Second, several applications in Web 2.0-based e-commerce will be discussed in detail. Last but not least, in view of the abundance of information nowadays, this module will encourage student discussions on the problem of finding the relevant “needle in the haystack" and the problem of information overload.