For more details on the courses, please refer to the Course Catalog
| Code | Course Title | Credit | Learning Time | Division | Degree | Grade | Note | Language | Availability |
|---|---|---|---|---|---|---|---|---|---|
| ICE3043 | Introduction to Smart Car Engineering | 3 | 6 | Major | Bachelor | 3-4 | Information and Communication Engineering | Korean | Yes |
| This course deals with a broad outline of smart cars. In order to understand the basic fundamentals of an automobile, this course firstly introduces the state-of-the-art technologies which are currently being developed. In addition, an overall automobile architecture including driving, transmission, and chassis systems is introduced. Then, the driver assistance system for providing the comfort to operators is informed. This course also presents the recent development trend of smart cars in terms of in-vehicle softwares and electronics including sensors, actuators, and embedded boards. Moreover, the novel technologies for control softwares of vehicle system, vehicle networking, and application level softwares of connected cars are covered. Finally, in this course, we debate on the social, legal, and ethical issues of the smart car technology for finding solutions of its faster development. | |||||||||
| ICE3044 | Basic Algorithm | 3 | 6 | Major | Bachelor | 3-4 | Information and Communication Engineering | - | No |
| 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. | |||||||||
| 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. | |||||||||
| ICE3046 | Establishing Business Field Training Program 1 for CICE | 3 | 0 | Major | Bachelor | 3-4 | Information and Communication Engineering | - | No |
| This subject is a Establishing Business field training program for 1 semester. Students are supposed to learn and experience practical knowledge as Company Representative (or Co-Representative) from fields. | |||||||||
| ICE3047 | Establishing Bunisess Field Training Program 2 for CICE | 6 | 0 | Major | Bachelor | 3-4 | Information and Communication Engineering | - | No |
| This subject is a Establishing Business field training program for 1 semester. Students are supposed to learn and experience practical knowledge as Company Representative (or Co-Representative) from fields. | |||||||||
| ICE3048 | Establishing Business Field Training Program 3 for CICE | 9 | 0 | Major | Bachelor | 3-4 | Information and Communication Engineering | - | No |
| This subject is a Establishing Business field training program for 1 semester. Students are supposed to learn and experience practical knowledge as Company Representative (or Co-Representative) from fields. | |||||||||
| ICE3049 | Establishing Business Field Training Program 4 for CICE | 12 | 0 | Major | Bachelor | 3-4 | Information and Communication Engineering | - | No |
| This subject is a Establishing Business field training program for 1 semester. Students are supposed to learn and experience practical knowledge as Company Representative (or Co-Representative) from fields. | |||||||||
| 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. | |||||||||
| ICE3054 | Artificial Intelligence Systems Laboratory | 2 | 4 | Major | Bachelor | 3-4 | Information and Communication Engineering | - | No |
| Practical exercises related to machine learning and artificial intelligence models. Learning programming tools (Visual Studio Code, Colab, Jupyter Notebook, etc.); Debugging exercises using programming tools (utilizing breakpoints for variable and tensor value analysis); Learning how to install and use necessary programs in a Linux environment (anaconda, virtual environment, docker, ssh, screen, tmux, etc.); Practical exercises on how to use visualization tools necessary for data analysis (such as using matplotlib for drawing plots); Practical exercises in basic Python and numpy for scientific computing; Practical exercises on using basic machine learning libraries (such as sklearn); Practical exercises on using and implementing basic deep learning libraries (tensorflow, pytorch, jax, etc.). | |||||||||
| ICE3055 | Big Data Analysis and Modeling | 3 | 6 | Major | Bachelor | 3-4 | Information and Communication Engineering | Korean | Yes |
| This course introduces big-data analysis and modeling technologies, particularly those producing meaningful results with various practical data resources. The course discusses big data system, storage, processing, analysis, and visualization techniques, theories of big data analytics, and computational methodologies for big data processing. | |||||||||
| ICE3056 | Foundations of Modern Artificial Intelligence | 3 | 6 | Major | Bachelor | Information and Communication Engineering | Korean | Yes | |
| This course covers foundations of modern artificial intelligence (AI), including architectures/modules of modern AI, training loss functions, and training algorithms. The course introduces representative applications using modern AI techniques. The topics of interests include but are not limited to the following: linear algebra review for AI, probability & random variable review for AI, fully-connected network, convolutional network, autoencoder, activation function, mean squared-error loss function, cross entropy loss function, supervised learning, transfer learning, (stochastic) gradient descent, overfitting and generalization, image classification, image segmentation, image denoising and diffusion model, time-series data and recurrent neural network, and natural language processing and transformer. The course includes programming practices and AI projects. | |||||||||
| 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. | |||||||||