Computer Science

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.

This course explores modern numerical algorithms through three connected tasks: large scale linear algebra, optimization for data science, and deep learning.  The first six lectures discuss how to approximately solve massive scale linear algebra tasks using techniques not covered in linear algebra courses. The second six lectures discuss optimization algorithms with a focus on large data science tasks. Numerical optimization is one of the most useful skills as so many tasks from science to business can be cast as optimization problems. The six seminars focus on deep learning, the key algorithmic advance driving the recent advances in machine learning and artificial intelligence. The lectures on numerical linear algebra and optimization ground this course in well understood numerical algorithms which students can study in detail, while the deep learning seminars give students the opportunity to explore the excitement driving the AI revolution. 

This course offers a comprehensive exploration into the field of Artificial Intelligence (AI), specifically designed for students with diverse backgrounds. Spanning a period of three weeks, participants are introduced to fundamental AI concepts and techniques, ranging from basic machine learning principles to advanced neural networks and ethical considerations. Through a mix of interactive lectures, hands-on coding exercises, and practical case studies, students not only acquire a theoretical understanding of AI but also develop practical skills in data pre-processing, model implementation, and ethical decision-making. The course serves as a platform for students to delve into AI's potential and ethical dimensions, cultivating insights into its applications across industries and nurturing a curiosity for further AI study.