Computer Science

This course is designed to provide a comprehensive introduction to the modern study of computer algorithms. It presents many algorithms and covers them in considerable depth. Each chapter presents an algorithm, a design technique, an application area or a related topic. Since we emphasize efficiency as a design criterion, we include careful analyses of the running time of all our algorithms. In addition to the introduction of “design of algorithms”, we also play the emphasis on the “complexity analysis of algorithms” to help students understand the detailed differences between various algorithms for a certain problem mainly in terms of time. The carefully chosen English material is intended to provide the students an enjoyable taste for the international class on algorithms. The textbook we chose is also used by many other universities for undergraduate algorithm course. The course targets the enhancement of the following skills: 1)understanding and mastering the fundamental algorithm design by a series representative algorithms such as: graph algorithms, sorting algorithms etc.; 2) training the capability of algorithms analysis as well the proof of the correctness of algorithms in terms of time complexity and asymptotic efficiency, improving the logic reasoning and understanding the development of algorithm theory; 3) encouraging students to have a depth understanding of studied algorithm by applying them to practical applications as well as problems, training them to relate what they have learned in the class to the real-world problems.4) improving the capability of solving real-world problems.

This course surveys research methods for science and technology studies and across the social sciences that contribute to the generation of new data. Students study a diverse range of methods and learn to understand the strengths and weaknesses of particular methods for investigating particular questions. Students are introduced to the theory and practice of qualitative and quantitative methods. Topics include research ethics, research design, face-to-face interviews and focus groups, surveys, content and discourse analysis, and ethnography. 

Students learn fundamental theories and methods of database systems: what they are, how they are developed, and how they function to achieve their purposes. The course exemplifies these constructs with contemporary database technologies and students learn how these technologies are exploited to build effective information systems of different scale.

The course helps students to become confident with a range of data structures and algorithms and able to apply them in realistic situations. The course provides the tools required to analyze a problem and decide which algorithms or algorithmic techniques to apply to solve it. The course involves practical programming and encourages a thoughtful approach to analysis and design problems.

The course presents an application-focused and hands-on approach to learning neural networks and reinforcement learning. It is an introduction to deep learning methods, presenting a wide range of connectionist models that represent the current state-of-the-art. Topics include the fundamentals of machine learning and the mathematical and computational prerequisites for deep learning; feed-forward neural networks, convolutional neural networks, and the recurrent connections to a feed-forward neural network; a brief history of artificial intelligence and neural networks, and reviews open research problems in deep learning and connectionism.  Entry requirements include 90 credits in statistics and a course in linear algebra.

Natural language Processing (NLP) is one of the most important technologies in Artificial Intelligence. NLP aims at enabling computers to understand human languages and communicate with humans. There are a large variety of tasks and machine learning methods in NLP. The course provides a thorough introduction to NLP, from its history to recent advances in deep learning applied to NLP. On the task side, we will cover sequence tagging, parsing, classification and clustering, and some applications such as machine translation. On the model side, we will cover statistical models and neural networks. By learning from lectures and programming assignments, students will master necessary knowledge about NLP and engineering tricks for practical NLP problem. 

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.