Computer Science

In addition to introducing the history and development of the Linux operating system, this course will also introduce the use of mainstream open-source software, so that students can proficiently use Linux and integrate it into their daily work and study. Through this course, students can not only master the use of the Linux operating system but also understand its underlying culture, learn about the open-source movement community, and gain a deeper understanding of computer systems themselves.

This course takes students on a journey through one of artificial intelligence's most dynamic fields. Deep reinforcement learning (DRL) has achieved remarkable breakthroughs, from mastering complex games to controlling robots. The course discovers how artificial intelligence (AI) agents learn to make decisions through interaction, beginning with core concepts in reinforcement learning and deep learning; then it explores how these powerful approaches combine to create sophisticated learning systems. 
 
The course progresses naturally through key topics in decision making with Markov processes, modern deep learning techniques for AI, value-based methods that help agents evaluate their choices, policy optimization approaches for learning effective behaviors, and advanced strategies for stable and efficient learning. The course emphasizes practical understanding through hands-on examples.  By the end of the course, students will understand how to build AI systems that can learn and adapt in complex environments. 
 

This course introduces the basic concepts of Cybersecurity. It explores the challenges that the interconnectedness of cyberspace poses to computer networks; the concept of risk; typical patterns of vulnerabilities, as well as attacks and mitigation strategies.  

The course introduces, in a non-technical fashion, the basic concepts of cryptography, and the typical cryptographic building blocks: encryption, digital signatures, authentication codes, public key and secret key infrastructures. The course discusses how these building blocks are used to construct secure networks and the legal frameworks handling cyber-attacks. Finally, the course analyzes cybersecurity in the context of Japan and East Asia. 

This course provides an overview of the different aspects and stages involved in the engineering of software with a special focus on architectural properties of large systems. Assuming that course participants are acquainted with basic software development principles, this course provides knowledge on and experience with the wider aspects and stages in the lifecycle of a (large) software system. It introduces the general principles of software engineering, methods for addressing software engineering problems, common tools and techniques for solving software engineering problems, and methods, tools, and techniques for designing software systems and their architecture. Topics include: project management; requirements elicitation; architectural analysis, description, synthesis, prototyping & evaluation; software design and development; software implementation; quality assurance; maintenance and evolution; software business.

This course introduces quantum computing from a computer science perspective, focusing on mathematical and algorithmic foundations. Quantum computers have the potential to solve difficult computational problems for which no efficient classical algorithms exist. Writing quantum algorithms is radically different from programming classical computers and requires an understanding of quantum principles and the mathematical foundations behind them. Course participants will gain practical experience by developing quantum programs in Qiskit and their simulation and execution on quantum processing units(QPUs) of the IBM Quantum Platform, particularly the Yonsei University Eagle QPU. 

Course goals: (1) Acquire a firm understanding of the quantum-mechanical foundations of qubit superposition, entanglement, and interference at the heart of all quantum computations. (2) Understand the early quantum algorithms such as Deutsch’s Problem, Bernstein-Vazirani, and Quantum FFT, and be able to code and execute them on a QPU. (3) Know recent near-term quantum algorithms like the quantum simulation of Hamiltonian dynamics. (4) Understand and control, in principle, the quantum circuit compilation pipeline and error mitigation techniques to execute near-term quantum workloads on QPUs. 

Prerequisites: An introductory programming class, e.g., CAS1100-01, is strictly required. A course in linear algebra is strictly required. 

This course uses Python as the medium to enable students to master the general ideas and methods of solving problems with computers. Students can master IPO (Input-Processing-Output) program structure, master basic control flow syntax, and be able to select data structures and related,  apply algorithms to complete simple computing tasks and have a solid programming foundation. For complex computational tasks, students can use a top-down modular decomposition approach to transform them into simple problem calculations. Students can use Python third-party libraries for data analysis and processing and AI applications (machine learning, natural language processing, etc.) solution, computer vision, etc.), and can be connected with more advanced artificial intelligence courses.

The course is designed for senior and graduate students majoring in Computer Science to learn design philosophy, practice, and research challenges for software design for smart medical sensing systems.   

Smart sensing systems have the capability of processing the sensing data on the device and the capability of providing the detected events as the outputs. This type of sensing system is required to generate accurate sensing events in real time. The systems are also required to minimize their energy consumption in specific application scenarios.  With smart sensing systems, the faults can be contaminated, the system can be more robust and easier to develop. Finally, the systems can be certified for medical use.   

This course covers model smart sensing devices, realtime computation, Computing-In-Memory devices, and communications between computing devices.  

This course covers the principles and practice of modern computer communications through studying network abstractions, protocols, architectures, and technologies at all levels of the five-layer reference model.

This course offers an introduction to computers with topics including: representation of digital information; specification and implementation of combinational systems; basic combinational modules; specification and implementation of sequential systems; basic sequential modules; design practices of combinational and sequential circuits.

This course will comprehensively introduce the basic concepts, mainstream structures, learning paradigms and key applications of deep learning technology based on neural networks that have been developed in recent years.