Computer Science

Python has rapidly become the standard in scientific computing. It is however much more than that, receiving much excitement about the application of Python to finance, medicine, mobile technology, online gaming, film industry. Its appeal continues to grow in both academia and industry. Much of the advances of medical technology has been due to Python. This is a an intensive Python programming course with numerous medical and health-based applications. Due to the transferability of these skills, students also study examples from investment banking and quantitative finance. The course assumes no prior knowledge of the Python programming language. However, an interest in biomedicine/health is essential.

This course provides an introduction to the quantitative analysis of data, blending classical statistical methods with recent advances in computational, and machine learning. Students cover key topics such as the challenges of analyzing big data using statistical methods, and how machine learning and data science can aid in knowledge generation and improve decision-making. Students also explore quantitative methods of text analysis, including mining social media and other online resources.

Computer graphics deals with the processing of visual images and spatial data by a computer. Lectures focus on the very basics of modeling and rendering, i.e., the mathematical description of three-dimensional scenes and how to create realistic images of such models. Foundations of computer graphics, such as transformations and projection of 3D models, hidden surface removal, triangle rasterization, shading, texture mapping, shadows, and ray tracing, and advanced topics in physically-based global illumination.  A brief review of the mathematical basics needed for computer graphics, including linear algebra and other areas of higher mathematics that are important far beyond the field of graphics is included.

Data is one of the most important assets of any enterprise and plays a central role in many aspects of everyday life, from healthcare, to education, to commerce. In order to be turned into meaningful information that enables and supports decision making, data must be stored, maintained, processed and analysed. Database management systems are complex software programs that allow their users to perform these tasks in an efficient and reliable way. This course is an introduction to the principles underlying the design and implementation of relational databases and database management systems.

This research internship program offers selected students the opportunity to participate in research projects or work as an intern in research centers or organizations at Yonsei University. Students are expected to participate in research projects for approximately 20 hours per week throughout the program. Projects will vary depending on placement. Graded Pass/No pass only.

This course introduces the field of parallel computing with hands-on parallel programming experience on real parallel machines. The course consists of four parts: parallel computation models and parallelism, parallel architectures, parallel algorithm design and programming, and new parallel computing models. Topics include: theory of parallelism and models; shared-memory architectures; distributed-memory architectures; data parallel architectures; interconnection networks, topologies and basic of communication operations; principles of parallel algorithm design; performance and scalability of parallel programs, overview of new parallel computing models such as grid, cloud, and GPGPU. The course requires students to take prerequisites.