Computer Science

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.

This course is a study of current topics related to ethics and legislation within the field of computer science and technology. Topics include: privacy; digital rights and inequality; copyright; cyber crime, security, and control; professional ethical codes.

This course covers the basic principles of machine reasoning, exploring the foundations of the rapidly developing field of artificial intelligence, and outlining the mathematical techniques used in both knowledge representation and future artificial intelligence courses. Once equipped with the main technical and theoretical tools, students are presented with a selection of different applications of machine reasoning, e.g., natural language processing, machine vision, and robotics, to create a point of contact with real-world examples and future, more advanced AI courses.

The course enables students to become skilled in the use of techniques and tools for modelling, implementing, and evaluating interactive systems, and they learn how to apply the theories, techniques, and tools presented in the course via challenging exercises which combine design, implementation, and evaluation.