For more details on the courses, please refer to the Course Catalog
| Code | Course Title | Credit | Learning Time | Division | Degree | Grade | Note | Language | Availability |
|---|---|---|---|---|---|---|---|---|---|
| SWE3027 | Introduction to Embedded Software | 3 | 6 | Major | Bachelor | 4 | Computer Science and Engineering | - | No |
| This course is aiming at the understanding of various techniques on embedded software. This course covers embedded system architecture, embedded platform booting, RTOS, embedded linux, graphics and multimedia acceleration, network connectivity, application platforms, debugging and performance tuning, etc. Preliminary courses : Computer architecture, Operating system, etc. | |||||||||
| SWE3049 | Introduction to Big Data Analytics | 3 | 6 | Major | Bachelor | Computer Science and Engineering | English | Yes | |
| This undergraduate course will focus on various techniques and algorithmic methods for big data analytics. The emphasis will be learning key data mining algorithms for analyzing massive data sets with theoretical analysis of the methods and their practical applications. Several hands-on exercises will be provided using Spark, Hadoop, Python, and Matlab where students will learn big data programming and applications. Also, using social networks and the World Wide Web as real-world big data applications, scalable graph mining techniques will be discussed. | |||||||||
| SWE3050 | Fundamentals of machine learning | 3 | 6 | Major | Bachelor | Computer Science and Engineering | English,Korean | Yes | |
| This course covers fundamental theory and concepts of machine learning. Topics include linear regression, logistis regression, k-nearest neighbor, Naive Bayes, decision tree, perception, multi-layer perception, deep neural networks, k-means, dimensionality reduction, density estimation, and matrix factorization techninque, and reinforcement learning basics. | |||||||||
| SWE3051 | Introduction to Computer Vision | 3 | 6 | Major | Bachelor | Computer Science and Engineering | Korean | Yes | |
| This course covers techniques to acquire, process, analyze, and understand visual data such as images and videos. The lecture is designed for 4th year undergraduate students to discuss fundamental principles, algorithms, and important applications of computer vision. The expected topics include image formation, basic image processing, camera models, feature extraction and matching, image classification, object detection and tracking, and recent methods based on deep learning. | |||||||||
| SWE3052 | Introduction to Deep Neural Networks | 3 | 6 | Major | Bachelor | 4 | Computer Science and Engineering | English,Korean | Yes |
| This course is designed to study the basic theory and basic structures of deep neural networks, which has been receiving a lot of attention recently. The main topics cover the various structure of neural networks, such as multi-layer perceptrons, convolutional neural networks, and recurrent neural networks, learning algorithms, various activation functions, regularization and normalization techniques. | |||||||||
| SWE3053 | Introduction to Human Computer Interaction | 3 | 6 | Major | Bachelor | 3-4 | Computer Science and Engineering | Korean | Yes |
| This course provides an introduction to and overview of the field of human-computer interaction (HCI). We learn to design, prototype, and evaluate user interfaces for computers upon the theories and methodologies from computer science, cognitive psychology, and human factors. Course materials include classic and recent research papers in HCI. We cover the following topics: human factors, usability, interaction elements, design principles, and evaluation techniques. | |||||||||