Computer Science

This course offers an introduction to software engineering including software modeling languages, the software development process--workflow modeling, project planning and management, and requirements analysis and specification--software requirements modeling.

This is a research-oriented course for cybersecurity, a broad, fast, evolving discipline. The course covers important concepts but it not meant to be comprehensive. The following topics will be covered:

• The applied aspects of cryptographic primitives (randomness, hash, MAC, encryption, digital signatures)
• Cryptographic protocols (key exchange, authentication, anonymous communication, privacy-enhancing technologies)
• Network security (TCP/IP, DNS, BGP, TLS, DDoS, wireless, email, MLS)
• Advanced topics: _ security (IoT, SDN, blockchain, web, software, systems,...)

Course prerequisites: Basic knowledge in discrete mathematics, programming, and networking is strongly recommended. Class participants are also expected to comprehend research papers and conduct a research project. 

This course serves as an introductory course to high-level language programming, specifically designed for students with no prior experience. It can also act as a foundational course for other information-related subjects. The goal is for students to grasp the fundamental concepts and methods of object-oriented programming, understand the basic syntax and programming techniques of C++, learn to use integrated development environments, master program debugging methods, gain a preliminary understanding of common data structures and non-numerical algorithms, and get an initial introduction to the usage of the C++ Standard Template Library (STL).

This course examines AI concepts, including how to interact with AI systems, and critically evaluate their impact. It covers the ethical, social, and technological dimensions of AI. 

This course will focus on the advanced technologies in 3D visual computing in the era of big data. The course will cover a brief introduction to the basic concepts in the geometric analysis including curve, surface, 3D representations, 3D transformations, etc., with the goal of fostering students’ geometric understanding of 3D data. …

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.