For more details on the courses, please refer to the Course Catalog
| Code | Course Title | Credit | Learning Time | Division | Degree | Grade | Note | Language | Availability |
|---|---|---|---|---|---|---|---|---|---|
| ISW2001 | Basic Mathematics for Machine Learning | 3 | 6 | Major | Bachelor | 2-3 | English | Yes | |
| This course introduces the fundamental mathematical concepts and theories necessary to understand machine learning and artificial intelligence. Beginning with the basic mathematical units and precision, it progresses to an understanding of widely used concepts in deep learning, including linear algebra, nonlinear functions, statistical measures, probability distributions, partial derivatives, convolutions, and performance evaluation metrics. The course concludes with a brief introduction to advanced topics such as entropy, quantization, multivariate random variables, and graph representations. This course focuses on the mathematical tools required for machine learning. | |||||||||
| ISW2002 | Digital Signal Processing Basics | 3 | 6 | Major | Bachelor | 2-3 | English | Yes | |
| This course covers the fundamental concepts of signal processing and various digital signal processing methods. In the first half, students will learn the basic concepts of systems, including characteristics of continuous and discrete signals, linearity, time invariance, and causality. The second half focuses on digital signals, covering topics such as sampling, aliasing, interpolation, frequency analysis, digital filters, and Fourier Transform. In particular, the course addresses techniques widely used in processing digital images and audio signals, which are key application areas in artificial intelligence that we frequently encounter. Additionally, the course includes Python-based programming labs where students implement important signal processing methods like the Fourier Transform. | |||||||||
| ISW3001 | Introduction to Quantum Computing and Systems | 3 | 6 | Major | Bachelor | 3-4 | Korean | Yes | |
| This course provides an introduction to fundamental quantum information theory, representative quantum applications, and recent trends in the development of large-scale quantum computers. - Quantum information theory: Superposition, entanglement, and quantum state measurement. - Quantum applications: Shor's algorithm, Quantum Fourier Transform (QFT), quantum phase estimation (QPE), and Quantum Machine Learning (QML). - Quantum computer systems: Control of physical qubits, quantum error correction, quantum compiler. | |||||||||
| ISW3002 | Introduction to Sequential Data Processing | 3 | 6 | Major | Bachelor | 3-4 | Korean | Yes | |
| This course covers the fundamental concepts and techniques required to process continuous or sequential data. Beginning with basic digital signal processing, the course introduces representative time-series analysis methods such as Kalman filtering, autoregressive modeling, and MCMC. The scope then extends to various domains, including speech, text, and video. The course introduces deep learning–based models for sequential data applications. | |||||||||
| ISW3003 | Open Source AI Practice | 2 | 4 | Major | Bachelor | 3-4 | Korean | Yes | |
| This practice-oriented course aims to build a clear understanding of core models and algorithms in deep learning through hands-on implementation using Pytorch library. Coverage spans the full workflow, from data processing and model building to training and inference. The course will provide students with foundational skills for open-source based AI development. | |||||||||
| ISW3004 | Artificial Intelligence Ethics | 3 | 6 | Major | Bachelor | 3-4 | English | Yes | |
| This course aims to understand AI ethics and explore and discuss various models and algorithms to address the issues. AI ethics explores various ethical principles, such as fairness, transparency, safety, stability, and accountability, aimed at preventing unpredictable tragedies that may arise when AI is utilized. This course then investigate the methodology and algorithms for addressing current and future related issues. | |||||||||
| ISW3005 | Probabilistic Graphical Models | 3 | 6 | Major | Bachelor | 3-4 | English | Yes | |
| Probabilistic Graphical Models (PGMs) is a field that combines probability theory and graph theory to structurally represent and reason about complex probabilistic relationships. This course approaches the fundamental concepts of probabilistic modeling from a graph-based perspective and aims to equip students with the ability to systematically handle uncertainty in real-world phenomena. Students will learn a variety of graph-based models, including Bayesian Networks and Markov Random Fields, and will acquire techniques for inference, learning, and prediction using these models. | |||||||||
| ISW3006 | Artificial Intelligence Seminar | 3 | 6 | Major | Bachelor | 3-4 | Korean | Yes | |
| This course invites experts from the research and industrial sectors in artificial intelligence and computer engineering to explore the latest technological trends and future directions. By engaging with real-world cases and professional insights, students develop the ability to think critically about the potentials and limitations of AI technologies. The course is conducted in a seminar format centered on lectures and discussions. Students examine technological and societal issues from diverse perspectives and participate in collaborative debates. Through this process, they gain the insight and problem-solving capabilities necessary for pioneering new technologies and business opportunities, while also strengthening their communication and teamwork skills. | |||||||||
| ISW3007 | Statistical Analysis | 3 | 6 | Major | Bachelor | 3-4 | Korean | Yes | |
| This course provides students with foundational statistical theories and analytical methodologies for extracting meaningful insights from data and supporting data-driven decision making. It covers a comprehensive range of topics, including descriptive statistics, hypothesis testing, statistical inference, experimental design, regression analysis, analysis of variance, and nonparametric methods. In particular, it also covers their application in real-world analytical contexts. There are no formal prerequisites. However, a basic understanding of mathematics and introductory statistics, along with experience in data processing and programming, will be beneficial for successful engagement with the course. | |||||||||
| IWS3033 | Fundamentals of Programming Languages | 3 | 6 | Major | Bachelor | Winter International Student Experience | - | No | |
| The Principles of Programming Languages course offers a thorough introduction to the fundamental concepts of programming languages. The course systematically explores design issues of various language constructs and analyzes the design choices made in some of the most common languages. Students will learn about different language categories, the progression of programming languages, and the principles of syntax and semantics in modern programming languages. The course also covers key programming concepts such as lexical and syntax analysis, names, bindings, type checking, scoping, data types, expressions, statements, and control structures. Additionally, it includes topics like subprograms, abstract data types, functional programming languages, and logic programming languages. This course aims to give students a solid foundation in the principles of programming languages and insight into the design decisions that shaped their development. | |||||||||
| SWE2001 | System Program | 3 | 6 | Major | Bachelor | 2 | Computer Science and Engineering | Korean,English | Yes |
| This course introduces the theory, design, and implementation methodology of various types of system softwares such as assembler, preprocessor(macro processor), linker, loader, and text editor. System software is closely related to hardware architecture and thus the central theme of this course is the relationship between machine architecture and system softwares. The recommended prerequisites for this course might include data structures and C/C++ programming languages. | |||||||||
| SWE2003 | Automata | 3 | 6 | Major | Bachelor | 2-4 | Computer Science and Engineering | - | No |
| This course covers formal language, automata, grammar, and computational which topics are fundamental in computer science. Specific topics includes finite automata, formal language, context-free grammar, push-down automata, pumping lemma, turing machine, chomski hierarchy, deterministic/non-deterministic, and computational complexity. | |||||||||
| SWE2015 | Data Structures | 3 | 6 | Major | Bachelor | 2 | Computer Science and Engineering | Korean,English | Yes |
| The purpose of this course is to introduce data structures necessary for solving computer-oriented real problem and principles and techniques for specifying algorithms. The interesting topics will include the following; arrays, stacks, quenes, linked lists, trees, graphs, sorting, hashing, and AVL trees. The recommended prerequisite course for this study might include Discrete Structure and C-language. | |||||||||
| SWE2016 | Algorithms | 3 | 6 | Major | Bachelor | 2 | Computer Science and Engineering | Korean,English | Yes |
| The purpose of this course is to introduce algorithms for solving problems in computer applications and basic principles and techniques for analyzing algorithms. The topics will include analyzing criteria, searching, sorting, graphs, polynomials, string matching, and hard problems etc. | |||||||||
| SWE2021 | Open Source Software Practice | 2 | 4 | Major | Bachelor | 2 | Computer Science and Engineering | English | Yes |
| The open source software (OSS) is like a treasure box for software (SW) education, of which the source codes are open that makes possible the computer programs reviewed, analyzed, and reused. The course handles the usage of Git/GitHub. | |||||||||