For more details on the courses, please refer to the Course Catalog
| Code | Course Title | Credit | Learning Time | Division | Degree | Grade | Note | Language | Availability |
|---|---|---|---|---|---|---|---|---|---|
| ADS5006 | Advanced Machine Learning | 3 | 6 | Major | Master/Doctor | 1-8 | Applied Data Science | Korean | Yes |
| This course provides a broad introduction to machine learning, datamining, and statistical pattern recognition. Topics include: (i) Supervised learning (parametric/non-parametric algorithms, support vector machines, kernels, neural networks). (ii) Unsupervised learning (clustering, dimensionality reduction, recommender systems, deep learning). (iii) Best practices in machine learning (bias/variance theory; innovation process in machine learning and AI). The course will also draw from numerous case studies and applications, so that you'll also learn how to apply learning algorithms to building smart robots (perception, control), text understanding (web search, anti-spam), computer vision, medical informatics, audio, database mining, and other areas. | |||||||||
| AIM5002 | Theory of Machine Learning | 3 | 6 | Major | Master/Doctor | 1-4 | Artificial Intelligence | - | No |
| MachineLearningisthestudyofhowtobuildcomputersystemsthatlearnfromexperience.Thiscoursewillgiveanoverviewofmanymodelsandalgorithmsusedinmodernmachinelearning,includinggeneralizedlinearmodels,multi-layerneuralnetworks,supportvectormachines,Bayesianbeliefnetworks,clustering,anddimension reduction. | |||||||||
| AIM5004 | Deep Neural Networks | 3 | 6 | Major | Master/Doctor | Artificial Intelligence | - | No | |
| In this class, we will cover the following state-of-the-art deep learning techniques such as linear classification, feedforward deep neural networks (DNNs), various regularization and optimization for DNNs, convolutional neural networks (CNNs), recurrent neural networks (RNN), attention mechanism, generative deep models (VAE, GAN), visualization and explanation. | |||||||||
| CHS5006 | Optimization and performance evaluation of 3D printing | 3 | 6 | Major | Master/Doctor | 1-4 | Challenge Semester | - | No |
| Evolution of 3D printing application area is slow due to difficulty in developing contents, optimization and evaluation deposit process. We will discuss optimization techniques and evaluation of deposit process for DED based powder metal 3D printing. A real data set will be used for application of theory learned from the class. Furthermore, deep learning and machine learning techniques will be also covered. | |||||||||
| CHS7001 | Introduction to Blockchain | 3 | 6 | Major | Bachelor/Master/Doctor | Challenge Semester | - | No | |
| This course deals with the basic concept for the overall understanding of the technology called 'blockchain'. We will discuss the purpose of technology and background where blockchain techology has emerged. This course aims to give you the opportunity to think about the limitations and applicability of the technology yourself. You will understand the pros and cons of the two major cryptocurrencies: Bitcoin and Ethereum. In addition, we will discuss the concepts and limitations about consensus algorithm (POW, POS), the scalability of the blockchain, and cryptoeconomics. You will advance your understanding of blockchain technogy through discussions among students about the direction and applicability of the technology. | |||||||||
| CHS7002 | Machine Learning and Deep Learning | 3 | 6 | Major | Bachelor/Master/Doctor | Challenge Semester | - | No | |
| This course covers the basic machine learning algorithms and practices. The algorithms in the lectures include linear classification, linear regression, decision trees, support vector machines, multilayer perceptrons, and convolutional neural networks, and related python pratices are also provided. It is expected for students to have basic knowledge on calculus, linear algebra, probability and statistics, and python literacy. | |||||||||
| CHS7003 | Artificial Intelligence Application | 3 | 6 | Major | Bachelor/Master/Doctor | Challenge Semester | - | No | |
| Cs231n, an open course at Stanford University, is one of the most popular open courses on image recognition and deep learning. This class uses the MOOC content which is cs231n of Stanford University with a flipped class way. This class requires basic undergraduate knowledge of mathematics (linear algebra, calculus, probability/statistics) and basic Python-based coding skills. The specific progress and activities of the class are as follows. 1) Listening to On-line Lectures (led by learners) 2) On-line lecture (English) Organize individual notes about what you listen to 3) On-line lecture (English) QnA discussion about what was listened to (learned by the learner) 4) QnA-based Instructor-led Off-line Lecture (Korean) Lecturer 5) Team Supplementary Presentation (Learner-led) For each topic, learn using the above mentioned steps from 1) to 5). The grades are absolute based on each activity, assignment, midterm exam and final project. Class contents are as follows. - Introduction Image Classification Loss Function & Optimization (Assignment # 1) - Introduction to Neural Networks - Convolutional Neural Networks (Assignment # 2) - Training Neural Networks - Deep Learning Hardware and Software - CNN Architectures-Recurrent Neural Networks (Assignment # 3) - Detection and Segmentation - Generative Models - Visualizing and Understanding - Deep Reinforcement Learning - Final Project. This class will cover the deep learning method related to image recognitio | |||||||||
| COV7001 | Academic Writing and Research Ethics 1 | 1 | 2 | Major | Master/Doctor | SKKU Institute for Convergence | - | No | |
| 1) Learn the basic structure of academic paper writing, and obtain the ability to compose academic paper writing. 2) Learn the skills to express scientific data in English and to be able to sumit research paper in the international journals. 3) Learn research ethics in conducting science and writing academic papers. | |||||||||
| ECE4280 | Wireless Networks Cornerstone | 3 | 6 | Major | Bachelor/Master | 1-4 | Electrical and Computer Engineering | English | Yes |
| This course provides an overview on wireless networking principles and technologies from the view point of computer science majors. The major themes will focus on the fundamentals and principles covering the protocol stacks of wireless networks, and wireless data services. | |||||||||
| ECE5613 | Special Topics on Information Security Theory | 3 | 6 | Major | Master/Doctor | 1-4 | Electrical and Computer Engineering | - | No |
| There will be studied on the classical cryptosystem, symmetric cryptosystem, public key cryptosystem, and zero-knowledge interactive proof system which are based on number theory, information theory and complexity theory. By using above cryptographic techniques, also, identification, authentication, digital signature, key management, and the security of communication and computer network will be lectured. | |||||||||
| ECE5947 | HCI Design | 3 | 6 | Major | Master/Doctor | 1-4 | Electrical and Computer Engineering | Korean | Yes |
| HCI, the general concept defining the interaction between the user and the product, suggests that the initiative of product development is shifted from the knowledge of the developer to the satisfaction of the user. Consequently the product should be designed such that it is more appealing to diverse user groups and its use is easy to learn. This lecture provides students with principles and techniques of HCI necessary to design HW and SW component of the product to the user’s satisfaction. | |||||||||
| EME4905 | Convergence Business Model Planning | 3 | 6 | Major | Bachelor/Master | 1-4 | Mechanical Engineering | Korean | Yes |
| This course amis to learn the methodologies for developing a new business model based on understandings on the needs of internet user group and network communities which are expanding the interconnections and creating new values. Students will study various technological trends such as IoT, cultural trends, big data, and methods for user study. This will be used to establish technology-converging business models and appropriate marketting strategies through multidisciplinary approach. | |||||||||
| EME5174 | Advanced Design and Manufacturing Engineering | 3 | 6 | Major | Master/Doctor | 1-4 | Mechanical Engineering | Korean | Yes |
| This course covers foundations for design theory and methodology such as design creativity, design cognition, and designers’ interactions and design for X – manufacture, assembly/disassembly, robustness, environment, etc. - methodologies with theoretical and project-based approaches. The advanced theories on traditional manufacturing technologies such as casting, forming, machining, welding and etc. will also be covered in addition to next generation technologies including micro/bio manufacturing, laser-based manufacturing, 3-dimensional rapid prototyping, and so forth. | |||||||||
| EME5308 | Combustion Processes | 3 | 6 | Major | Master/Doctor | 1-4 | Mechanical Engineering | Korean | Yes |
| This course will cover the fundamentals of combustion systems, and fire and explosion phenomena. Topics covered include thermochemistry, chemical kinetics, laminar flame propagation, detonations, energy and mass transport, and drop combustion. | |||||||||
| ERC5001 | Global Collaboration Research | 3 | 6 | Major | Master/Doctor | Engineering | Korean | Yes | |
| This class aims to enhance global competence as a researcher who study innovative growth fields through overseas dispatch of graduate students. Student’s who are dispatched overseas and carrying out joint research in world university will learn - to establish global network for future research - to strive for the creation of new industries in the field of sustainable development and innovative growth of mankind - to grow into global innovative leader with the competence of ‘Value creation’, ‘Convergence’, ‘Innovation’, and ‘Collaboration’ through innovation in creative convergence experiences that which cross the intercultural, interdisciplinary and intergeneration. In this class, students are expected to focus on projects during the overseas dispatch period to improve the quality of their research. Each researcher will present final report after they complete dispatch study and will be given their credit based on the presentation. | |||||||||