Computer Science

This course introduces data processing in detail from the aspects of data calculation, sorting, query, screening, statistical summary, and chart generation, combined with some VBA programming practice. It improves students' data processing ability and enables non computer majors to learn the thinking and methods of computer programming.

Computational Thinking is a process of solving problems typically with four steps—decomposition, pattern recognition, abstraction, and algorithmic thinking. This course concentrates on algorithmic thinking and examines how to reformulate problems with step-by-step procedures to solve the problems. Students then practice the implementation of the procedures with Python programming language in their homework assignments. This course also covers various paradigms in designing the procedures such as divide-and-conquer, greedy methods, dynamic programming, backtracking, branch-and-bound, etc., along with fundamental data structures such as linked-lists, stacks, queues, recursion, graphs, trees, binary heaps, and hashing. 

Companies such as Amazon, Airbnb, and LinkedIn build and manage powerful supply networks to create value. This course provides an understanding of these networks and their relationships with customers as well as suppliers. The digitization and innovation processes that govern these relationships are also examined. Students critically evaluate cutting-edge thinking on these topics and discuss implications for supply chain management, strategy, and marketing.

This course provides a complete understanding of basic programming concepts and how to implement them in C Sharp (C#). The course emphasizes the major features of the programming languages to solve problems in engineering. This course includes lab sessions which followed by lectures.

This course deals with a series of recent issues in artificial intelligence (AI) focusing on the field of design, more specifically deep learning and architectural space design, for beginners. Students review the related technologies with cases, and the conceptual and intellectual issues on top of AI in the perspective of design. Not only focusing on the AI techs, but also surveying the qualitative/quantitative aspects of design with theoretical issues outside of the conventional state of knowledge are the objectives of this course, empowered by actual individual project developments. Theory lectures, case studies, survey on the references, and students’ participation in class are the materials for the course. In the technological standpoint, recent decade has marked a huge change in how we perceive and talk about general AI. Buzz words “Big Data” and “Machine Intelligence” also changes (or will change) the fundamental role of designers form conventional approaches, and we will take a look where to go via this course. The deep learning (DL) techniques, for example, have shown how end-to-end differentiable functions can be learned to solve complex design tasks involving high-level perception abilities. In association with this shift and effect to our domain-specific knowledge, design, we would keep eyes opening so that we can take max advantages from it.