Computer Science

This course provides computational skill development. It examines computational problem formation and tools for problem solving (Excel and Python). Other topics include decomposition and pattern recognition, abstraction and algorithm design, information utilization and storage (tables and databases), information presentation (data visualization), ethical usage of digital content and technology, and privacy. 
 

This course explores the basic concepts of artificial intelligence principles and programming. It practices theories on machine learning, deep learning, and reinforcement learning through simple examples and solves various (social) problems using artificial intelligence as a tool. Topics include Python Basics, NumPy, and Matplotlib.

This course offers a study of programming technology. Topics include: object oriented design and patterns; generics and creation of collections; graphical user interface; model view controller threats; programming with threads. 

This course examines basic data structures and algorithms that are commonly used in software development, and introduces students to broad concepts from Computer Science that enable them to develop effective programs. 

This course teaches how to understand Python programming; how to solve problems with Python, and how to analyze data and predict results with Python.  The course covers how to install Anaconda, a local platform to write Python programs, and use Colab by Google, a cloud Python platform; basic commands and rules in Python, and Python-related packages and libraries, such as NumPy, Matplotlib, Pandas, and Scikit-Learn. The course also instructs on how to clean data, analyze data, and predict data with Python and its related libraries. Students will have the opportunity to practice and write their code with Python to check their learning. 

The latest industrial revolution is named as Industry 4.0, which is defined as the combination of smart manufacturing systems and developed information technologies. The success model of Industry 4.0 is enabled by a group of tools such as cloud computing, machine learning, big data, internet of things, and cyber physical systems. This course provides a study of Industry 4.0 and its revolutionary implications to smart manufacturing, smart products/services, and smart cities. The implementation, opportunities and challenges of Industry 4.0 are also discussed. The powerful change in production techniques will require the extensive use of digital intelligence in the entire production process. As one of the important manufacturing methods of Industry 4.0, additive manufacturing (AM) or three-dimensional (3D) printing is introduced in the second part of course. 3D printing offers numerous benefits to a smart factory, such as high production efficiency, time and material saving, rapid prototyping, and decentralized production methods. This course provides a comprehensive study on the liquid, solid and powder-based 3D printing methods. It also offers insights on the applications and future trend of 3D printing.

This course provides an overview of the fundamental principles and important applications of computer vision. Topics include image processing, segmentation, feature extraction, photometric vision, motion and tracking, camera models, scene reconstruction, and human/scene/object recognition and detection.

This introductory course addresses basic concepts and linguistic processing methods regarding natural language comprehension and machine translation. It discusses fundamental concepts in information retrieval, text processing, and natural language understanding. Specific topics include morphological analysis, syntactic parsing, semantic analysis, pragmatics, and language generation.

Automatic methods of Information Retrieval (IR) have gained greater significance in recent years due to the dramatic increase in the amount of data available on the Web. The data is often present in multiple forms (such as text, image, video) and hence it is necessary that the IR techniques being deployed on the web are able to perform various operations such as search and retrieval across all these different data formats. In this course, the study of IR is focused on the methodologies of indexing, processing, and querying of primary textual data and extended to video and image data in the latter part of the course.

Decentralized blockchain-based systems, such as Bitcoin and Ethereum, are successful beyond all expectations. Although still in their infancy, they promise to revolutionize how we think of financial, information, and other infrastructures. This course covers the technical aspects of public distributed ledgers, blockchain systems, cryptocurrencies, and smart contracts. It covers how these systems are built, how to interact with them, and how to design and build secure distributed applications.