For more details on the courses, please refer to the Course Catalog
| Code | Course Title | Credit | Learning Time | Division | Degree | Grade | Note | Language | Availability |
|---|---|---|---|---|---|---|---|---|---|
| ICE3045 | Introduction to Machine Learning | 3 | 6 | Major | Bachelor | 3-4 | Information and Communication Engineering | Korean,Korean | Yes |
| Machine learning is the science that gives computers the ability to learn. Machine learning algorithms can learn from existing data and predict on future data. Machine learning techniques have been already applied to many areas including as self-driving vehicles, face recognition, speech recognition, and medical diagnosis. This course will provide the basic concepts and algorithms of machine learning and how to implement them. You will learn about linear & logistic regression, bias & variance, supervised learning such as support vector machines, kernels, and neural networks, and unsupervised learning such as clustering, dimensionality reduction, and deep learning. | |||||||||
| ICE3050 | Cornerstone Design: Advanced Machine Learning | 3 | 6 | Major | Bachelor | 4 | Information and Communication Engineering | - | No |
| This course is targeted to the students who have already taken the ‘Introduction to Machine Learning’ course. Students will be exposed to more advanced machine learning techniques and carry out several practical homework assignments as well as the final term-project. More specifically, after a brief review on basic machine learning, methods using deep neural networks (a.k.a. deep learning) will be introduced; examples will include multi-layer perceptrons (MLP), convolutional neural networks (CNN), and recurrent neural networks (RNN). Homeworks will consist of programming assignments on practical application such as image classification or natural language processing. Furthermore, final term-project will be carried out. [Prerequisites: linear algebra, probability and random processes, basic programming, introduction to machine learning] | |||||||||
| ICE3051 | Autonomous driving capstone design | 3 | 6 | Major | Bachelor | 3-4 | Information and Communication Engineering | Korean,Korean | Yes |
| A capstone design course that performs a team project to develop an autonomous car. In this class, students form a team with other students to develop a self-driving car with Jetson nano board and sensors, such as camera. In this class, students learn AI programming and problem-solving skills related to autonomous driving. | |||||||||
| ICE3052 | Autonomous driving using artificial intelligence and control | 3 | 6 | Major | Bachelor | Information and Communication Engineering | - | No | |
| This course will introduce artificial intelligence and control technologies for autonomous driving that are crucial in future mobility. The course will cover object detection neural networks, semantic segmentation neural networks, vehicle localization, path planning, driving behavior cloning, etc. In addition, the course will include a team project that uses a driving simulator. | |||||||||
| ICE3053 | Field Practice in Future Vehicles | 1 | 0 | Major | Bachelor | Information and Communication Engineering | - | No | |
| This course provides students applying for Future Vehicle Micro Degree a program that connects academic knowledge and field experience during semester or vacation. Students can apply their academic knowledge and gain career-building experience in the field for four to six weeks. | |||||||||
| ISS3183 | Human Computer Interaction | 3 | 6 | Major | Bachelor | English | Yes | ||
| This course covers the basic concepts, fundamental theories and current researches in humancomputer interaction. Topics include principles, theories, methodologies, design, implementation, evaluation and research in computer interfaces. The objectives of this course are: to familiarize students with basic concepts of human computer interaction; to introduce students to theories and principles in computer interface design; to develop students’ ability to design, conduct and analyze user studies for computer software; and to provide students with the knowledge of the design process for user interfaces. | |||||||||
| ISS3198 | Artificial Intelligence | 3 | 6 | Major | Bachelor | English | Yes | ||
| This course aims to teach the fundamentals of artificial intelligence starting with the concepts of intelligence, rationality and intelligent agents. Next, it will probe into problem solving, introducing the notion of search by drawing examples from puzzles and games amongst others. Then, the basics of knowledge representation and reasoning, such as logic and planning will be explored. Machine learning, a fast growing subfield of A.I. will also be covered focusing on technologies and real-world applications such as games, biomedical applications, social networks and smart technologies. Further topics (time-permitting) include the impact of major A.I. areas such as robotics and computer vision, natural language and speech processing in our society today. This is an introductory course and would be suitable for anyone interested to delve deeper into A.I. in the near future. Students will be given assignments that do not require any programming. | |||||||||
| 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. | |||||||||