For more details on the courses, please refer to the Course Catalog
| Code | Course Title | Credit | Learning Time | Division | Degree | Grade | Note | Language | Availability |
|---|---|---|---|---|---|---|---|---|---|
| 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 | Korean | Yes | |
| 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. | |||||||||
| DHC5029 | Data and AI-based Digital Health | 3 | 6 | Major | Master/Doctor | 1-8 | Digital Health | Korean | Yes |
| Digital health is defined as an academic branch to develop theories and methods for efficient analysis of biomedical big data and use of AI to discover medical values for personalized medicine. As such, it is quite an interdisciplinary area that involves biomedical informatics, computing, mobile healthcare and smart hospital system. In this course, we will overview basic contents of various topics in digital health. Topics covered will include: (1) history of digital health, (2) examples of digital health, (3) remote patient care & monitoring, (4) mobil health and wellness applications, (5) digital medical devices, (6) big data analytics, (7) AI (8) personalized medicine and genomics, (9) medical Imaging, (10) EMR and EHR, (11) interoperability (semantics, ontology), and (12) smart hospital. | |||||||||
| DHC5031 | Medical Informatics | 3 | 6 | Major | Master/Doctor | 1-8 | Digital Health | - | No |
| Medical Informatics is defined an academic branch to develop theories and methods for efficient management of various information and data used in patient care, medical education & research, and medical administration. As such, it is quite an interdisciplinary area that involves Cognitive Science, Decision Theory, and Information & Computer Science. Its application is, in principle, how we can apply information and communication technology to real clinical practices. In this course, we overview the basic contents of various topics of Medical Informatics. More specifically, we cover the following: (1) history of Medical Informatics, (2) characteristics of medical records, (3) patient care & monitoring, (4) Clinical Decision Support, (5) system design and engineering in health care, (6) Standards in Medical Informatics, (7) Information Retrieval and Natural Language Processing (NLP), (8) Medical Imaging, (9) Assessment and evaluation of technology, (10) EHR, (11) health care Administration, (12) Telemedicine, (13) Computer in Medical Education, and (14) Bioinformatics. | |||||||||
| DHC5032 | Computer Programming | 3 | 6 | Major | Master/Doctor | 1-8 | Digital Health | - | No |
| In this course, we introduce the fundamentals of computer programming. More specifically, students learn about basic structures of computers, structures and concepts in programming languages, and basic patterns in computer programming. To be able to apply the knowledge learned, the course also includes practical excercises to solve realistc problems arising in digital health. | |||||||||
| DHC5036 | Medical Machine Learning | 3 | 6 | Major | Master/Doctor | 1-8 | Digital Health | - | No |
| As health data have been digitalized, health big data have been highlighted. To analyze health big data, statistical methods were used traditionally. Recently, machine learning methods are widely applied in diverse health data. In thus course, machine learning will be introduced to analyze health big data. The course includes the basic concept, popular algorithms of machine learning, data preparation, and result interpretation. The necessary programming practice is followed. | |||||||||
| EBM5063 | Advanced Experimental Design | 3 | 6 | Major | Master/Doctor | 1-8 | Biomechatronic Engineering | Korean | Yes |
| This course deals with methods of experiment planning, data collection, error control, and treatment design for researches of life science and technology. The course covers basic concepts of experimental design, data processing, and statistical data analysis. | |||||||||
| ECE5916 | Digital Integrated Circuits | 3 | 6 | Major | Master/Doctor | 1-5 | Electrical and Computer Engineering | English | Yes |
| It covers structures and operational principles of CMOS transistors and digital citcuits (INV, NAND, NOR, LATCH, Current Mirror), computation of sizing and delays, Flash A/D converter. | |||||||||
| ECH5102 | Advanced Biochemical Engineering | 3 | 6 | Major | Master/Doctor | 1-4 | Chemical Engineering | - | No |
| Have chemical engineers become more familiar with the biological science, especially microbiology and biotechnology, evolving approach to chemical process analysis, control, and design to solve the problems and constraints associated with large-scale biotechnological production by aerobic ar anaerobic microorganisms. Students will recognize and practice how to design biochemical process, bioreactor, and contol systems with basic data obtained from ethanol fermentation experiments using Saccharomyces and Zymmomonas. | |||||||||
| ECH5121 | Nanomedicine | 3 | 6 | Major | Master/Doctor | 1-4 | Chemical Engineering | - | No |
| Nanomedicine, an offshoot of nanotechnology, is emerging as the core technology to surmount the limitations of the conventional therapeutic and diagnostic agents. For therapeutic nanomedicine, the lecture will cover the approaches to maximize the therapeutic efficacy and to minimize the side effects. For diagnostic nanomedicine, the imaging agents to detect the intractable diseases using various imaging modalities (MRI, CT, US, Optical Imaging) will be introduced. In addition, the theranostic nanoparticles for simultaneous therapy and diagnosis will be discussed. | |||||||||
| GBE4003 | BME Advanced Medical Imaging | 3 | 6 | Major | Bachelor/Master | Biomedical Engineering | English | Yes | |
| his class offers basic introduction of magnetic resonance imaging (MRI). As MRI do not involve any injection of special dye or radio-active isotope, it can easily apply into many clinical and research environment. This class discuss what is MRI with emphasis on physical principles. This classes also introduce real examples of MRI application in clinical and research settings. | |||||||||
| GBE4004 | biomedical imaging and reconstruction | 3 | 6 | Major | Bachelor/Master | Biomedical Engineering | - | No | |
| This course is to provide undergraduate and graduate students a chance to learn fundamental principles of medical imaging particularly focusing on tomographic imaging system (CT, MRI, PET). To this end, we deal with imaging physics for signal generation and acquisition, mathematical models and optimization for signal encoding and image reconstruction, and metrics for validation. | |||||||||
| GBE4005 | Principles of Human Brain Mapping | 3 | 6 | Major | Bachelor/Master | Biomedical Engineering | English | Yes | |
| Thiscoursewillfocusonhowfunctionalmagneticresonanceimaging(fMRI)isusedtounderstandhumanbrainfunction.WewillfirstexaminewhatfMRIis,howthemachineworks,andhowdataisgeneratedandprocessed.Next,wewilldiscusshowfMRItechnologycanbeusedtogainunderstandingofhowthehumanbrainoperates,bycoveringtopicsofexperimentaldesign,analysis,andproblemsinherenttobrainimagingresearch.Asaclass,wewillcollectadatasetoffMRIscans,andstudentswillhaveanopportunitytobeanexperimentalsubject.Followingdatacollection,wewillconductlabsessionswherestudentswilllearntoanalyzefMRIdata,runstatisticaltests,andwriteupexperimentalresults. | |||||||||
| GBE4008 | Human-level Artificial IntelligenceⅠ | 3 | 6 | Major | Bachelor/Master | Biomedical Engineering | - | No | |
| Artificial Intelligence (AI) has witnessed the unprecedented advance for the last decade. However, it still falls short of human-level intelligence in many aspects. For example, generalizing learned knowledge to novel situations, learning from a small number of data points, understanding other agents’ decisions and values, etc. With the current AI, we, as human beings, might not be able to enjoy our future with AI. To overcome these limitations, we need a deep understanding of the human brain and Intelligence, and further, we need to consider how all living organisms have survived with their adaptive behaviors. Thus, in this class, we will envision the future of AI by examining the intelligence of humans and living organisms. This class is recommended to students who are dreaming of future AI inspired by the human brain. | |||||||||
| GBE4009 | Human-level Artificial IntelligenceⅡ | 3 | 6 | Major | Bachelor/Master | Biomedical Engineering | - | No | |
| Artificial Intelligence (AI) has witnessed the unprecedented advance for the last decade. However, it still falls short of human-level intelligence in many aspects. For example, generalizing learned knowledge to novel situations, learning from a small number of data points, understanding other agents’ decisions and values, etc. With the current AI, we, as human beings, might not be able to enjoy our future with AI. To overcome these limitations, we need a deep understanding of the human brain and Intelligence, and further, we need to consider how all living organisms have survived with their adaptive behaviors. Thus, in this class, we will envision the future of AI by examining the intelligence of humans and living organisms. This class is recommended to students who are dreaming of future AI inspired by the human brain. | |||||||||