Computer Science

This course introduces students to the core ethics concepts needed to build better technology and reason about its impact on the economy, civil society, and government. In the first half of the course, students consider ethical questions raised by different steps in the data science pipeline, such as: What is data, and how can we design better (ethical?) data governance regimes? Can technology discriminate? If so, what are promising strategies for promoting fairness and mitigating algorithmic bias? Can we understand black-box AI systems and explain their decisions? Why is it morally important that we do so? In the second half of the class, students consider ethical questions raised by the use of AI systems to manage our work, political, and social lives.

This course provides a study of how to build Python programs using fundamental programming structures like variables, conditional logic, looping, and functions. It looks at how to process data using lists, tuples, and dictionaries in Python programs; and how to read and write files in Python for data analysis. It explores problem-solving procedures, data representations, and algorithms through computers, and teaches practical programming techniques that can express the problem-solving process in Python programming language.

This course focuses on the methods and techniques for efficient management (modelling, manipulation, and retrieval) of data and information. It provides a foundation for later courses in database management and advanced information management. Students describe and use UML for information modeling; describe and use XML techniques for data modeling and querying; describe techniques for exposing and retrieving information on the web semantic web/linked data approaches; and understand the ongoing collaborative process of eliciting ethical implications which influence technology design.

Modern information science has given rise to techniques for acquiring and analyzing a variety of observable data in order to reveal multimodal characteristics of humans. The objective of this course is to study sequential data analysis, machine learning, relation to cognitive science, and its applications through information processing represented by natural language processing, EEG, and speech.

This course is a core course in the field of computer science. The focus of the course is to introduce students to the basic knowledge and algorithms in the field of artificial intelligence, and guide them to use artificial intelligence models to solve real-world problems in the era of big data through practical projects in the course.

Brief Introduction to the Course Content: The content of this course mainly covers several basic modules in the field of artificial intelligence, from simple to complex, gradually explaining the principles and techniques used by intelligent agents to solve real-world problems. Specific content includes: search algorithms (basic search algorithms for trees and graphs, etc.), Markov decision processes (Markov models, etc.), game algorithms (Alpha-Beta pruning techniques, etc.), uncertain information reasoning methods (Bayesian models), and knowledge representation methods (first-order, higher-order, and semantic representations, etc.). Through the combination of course practice, students will be provided with means and methods to solve real-world big data problems.

This course requires students to have the ability to write simple programs using Python or be able to quickly grasp the use of Python. Prerequisites for the course are computer data structures and foundations of probability theory. By studying this course, students will understand some core issues and applications in the field of artificial intelligence, and master the relevant principles and algorithms. In addition, students will acquire the ability to write, maintain, and test Python language, and be able to use Python to solve real-world problems.

This class provides a comprehensive overview of artificial intelligence and explores successful cases of problem-solving in various domains, attempting to solve real problems. This course explores how AI works in general, looks at different AI algorithms/models, and allows for practice building AI Models.

This course looks at the challenges and techniques involved in programming multicore systems. The course starts out with a brief history of computing to motivate the shift to multicore architectures. Parallelism, execution indeterminism, thread-and-lock-based programming, non-blocking synchronization, and HW acceleration with GPGPUs are introduced in a step-by-step approach that is accompanied by individual programming assignments. The impact of hardware architectures on programmability and performance is highlighted. Emerging trends such as Stream-parallel programming and hardware transactional memory are introduced. 

Mobile and pervasive intelligence enables diverse smart applications in our daily life. It provides new insights into unstructured and uncertain information from a variety of sensors, data sources, user devices, and mobile platforms. The lecture covers theoretical fundamentals in sensing, communications, computing, and autonomy techniques; how to apply them in practical systems, and design principles in mobile and pervasive applications. The content includes the following topics:

A: Sensation and perception of mobile platforms
Section 1-Sensing: Wireless, visual, acoustic, and privacy-preserving sensing techniques
Section 2-Communications: Advanced communication and networking technologies to connect hardware and software components in one or more pervasive systems.
 

B: Intelligence creation
Section 3-Computing: Context-aware computing, serverless computing, and distributed intelligence
Section 4-Autonomy: Autonomous coordination and collaboration techniques between mobile platforms (e.g., drones or robots)
 

C: Hands-on tutorials 

Teaching Objectives: Through the study of these contents, students will understand the basic concepts of multimedia technology, master the fundamental theories of multimedia technology, and the usage of commonly used multimedia tools. They will also gain knowledge on multimedia software development and multimedia production, laying a solid foundation for future research and development in the field of multimedia. Students will also learn about the current status and practical demands of multimedia technology, professional ethical requirements for multimedia practitioners, and ethical influences.

Overview: This course provides a comprehensive introduction to multimedia technology, including its definition and key characteristics, acquisition and processing of audio-video information, multimedia data compression and encoding techniques, and multimedia network communication technology.