For more details on the courses, please refer to the Course Catalog
| Code | Course Title | Credit | Learning Time | Division | Degree | Grade | Note | Language | Availability |
|---|---|---|---|---|---|---|---|---|---|
| ISS3222 | Introduction to Machine Learning | 3 | 6 | Major | Bachelor | English | Yes | ||
| Covers fundamental concepts for intelligent systems that autonomously learn to perform a task and improve with experience, including problem formulations (e.g., selecting input features and outputs) and learning frameworks (e.g., supervised vs. unsupervised), standard models, methods, computational tools, algorithms and modern techniques, as well as methodologies to evaluate learning ability and to automatically select optimal models. Applications to areas such as computer vision (e.g., characte r and digit recognition), natural language processing (e.g., spam filtering) and robotics (e.g., navigating complex environments) will motivate the coursework and material. | |||||||||
| ISS3224 | Data Visualization | 3 | 6 | Major | Bachelor | English | Yes | ||
| This course explores the field of data visualization. Topics cover the expanse of visualization from data preparation and cleaning to visualization types such as time series, box plots, and violin plots. Included in our study are visualization tools, online interactive visualizations, and other issues related to the display of big data. | |||||||||
| ISS3233 | Statistics in Python | 3 | 6 | Major | Bachelor | 1-4 | English | Yes | |
| This course will cover elementary topics in statistics using Python. The statistics topics include principles of sampling, descriptive statistics, binomial and normal distributions, sampling distributions, point and confidence interval estimation, hypothesis testing, two sample inference, linear regression, and categorical data analysis. Using Python, students will learn basic knowledge in Python programming, data management, data formats and types, statistical graphics and exploratory data analysis, and basic functions for statistical modeling and inference. | |||||||||
| ISS3287 | Understanding Game Theory | 3 | 6 | Major | Bachelor | English | Yes | ||
| This course is intended to familiarize economics majors with game theory and its applications. It first considers how to set up and solve games. It then considers topics such as strategic entry deterrence, strategic choice of managerial incentives, games between a principal and an agent, auctions, bargaining, strategic trade policy, public goods, and club goods. | |||||||||
| ISS3290 | Introduction to Big Data Analysis | 3 | 6 | Major | Bachelor | English | Yes | ||
| Understand the genesis of Big Data Systems • Understand practical knowledge of Big Data Analysis using Hive, Pig, Sqoop • Provide the student with a detailed understanding of effective behavioral and technical techniques in Cloud Computing on Big Data • Demonstrate knowledge of Big Data in industry and its Architecture • Learn data analysis, modeling and visualization in Big Data systems | |||||||||
| SEE4001 | Special topics in semiconductor devices and processing | 3 | 6 | Major | Bachelor/Master | 1-4 | Semiconductor Convergence Engineering | Korean | Yes |
| On-site processing required for semiconductor device manufacturing experts give lectures directly so that students acquire the knowledge they need in the field. Additionally, through this lecture, students understand the physical operating principles and structure of manufacturing equipment. As semiconductor devices become highly integrated, we look at recent process and equipment development trends and problems with current processes that have become issues. | |||||||||
| SEE4002 | Artificial Intelligence Semiconductor Process Technology | 3 | 6 | Major | Bachelor/Master | 1-4 | Semiconductor Convergence Engineering | - | No |
| This course helps to understand the overall artificial intelligence semiconductor processes by introducing the theory and the application of unit processes; photolithography, photo-mask, dry-etch, cleaning, chemical-mechanical polishing(CMP), diffusion and thin film, and module processes; transistor, isolation, capacitor, interconnection. This also suggests the direction of artificial intelligence semiconductor process technologies for the future generations. | |||||||||
| SEE4003 | AI Semiconductor Device Simulation | 3 | 6 | Major | Bachelor/Master | 1-4 | Semiconductor Convergence Engineering | Korean | Yes |
| We introduce the latest semiconductor technologies such as ultra-low power logic operation and multi-function memory operation, which are the characteristics required for semiconductors to run artificial intelligence algorithms, and optimize semiconductor technology through actual simulation based on our understanding of these. The electrical characteristics of 3D semiconductor devices due to semiconductor device scaling, ultra-low power semiconductor devices due to new charge transport mechanisms, and memory devices based on various new devices are confirmed through actual simulation exercises and optimized and adjusted by adjusting device design parameters. Check the impact on the operation of the artificial intelligence algorithm. Through this course, you can develop an understanding of the latest semiconductor device theory, especially artificial intelligence semiconductor device theory, and develop not only theory but also basic practical skills by selecting practical topics that can be frequently experienced in the actual industry and carrying out projects. There is a purpose. Prerequisite courses include physical electronics, semiconductor engineering, semiconductor device design, and electronic circuits. | |||||||||
| SFC4001 | Smart Factory Capstone Design 1 | 3 | 6 | Major | Bachelor/Master | Smart Factory Convergence | Korean | Yes | |
| This corporate-sponsored projects course in smart factory is an industry-university partnership that integrates design, manufacturing, service engineering, and business realities into the engineering curriculum. Students take their project ideas from concept to reality by designing, prototyping, and simulating real solutions in state-of-the-art facilities. This course challenges students to apply the knowledge and tools acquired during their undergraduate education to solve real-world engineering problems. | |||||||||
| SFC4002 | Smart Factory Capstone Design 2 | 3 | 6 | Major | Bachelor/Master | Smart Factory Convergence | Korean | Yes | |
| This course provides a unique opportunity for industry to partner with our university to educate the next generation of world-class engineers. Interdisciplinary teams of students work together to tackle projects sponsored by industrial clients. These teams collaborate with engineering faculty, who serve as mentors and advisers, to devise ideas to solve engineering problems. | |||||||||
| SSE2035 | Introduction to Electric and Electronic Circuits | 3 | 6 | Major | Bachelor | 2-4 | Semiconductor Systems Engineering | Korean | Yes |
| This course introduces the basic concepts of electric and electronic circuits for students majoring software engineering. The topics included in this course are electric circuit elements, Ohm's law, Kirchhoff's law, Thevenin and Norton theorems, first-order circuits, Laplace transform and linear circuit analysis, linear amplifiers, operational amplifier, digital logic family, combinational circuits, sequential circuits, datapath circuit elements. | |||||||||
| SSE3061 | Digital System Design | 3 | 6 | Major | Bachelor | 3 | Semiconductor Systems Engineering | Korean | Yes |
| This course provides advanced techniques for design of digital systems. Sequential and asynchronous digital circuits are introduced, and design techniques for those circuits are covered. Basic modules such as multipliers, floating point arithmetic, state machines, and their control blocks are studied. Based on the concepts introduced so far, design methodologies for more complex digital systems are covered. Standard hardware description languages such as VHDL or Verilog, and design automation tool flow are also covered so that students can cope with today's design environment. Only the students who studied "Logic Design" can take this lecture. | |||||||||
| SWE2001 | System Program | 3 | 6 | Major | Bachelor | 2 | Korean,English,Korean | 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 | English | Yes | |
| 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 | English,Korean | 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. | |||||||||