For more details on the courses, please refer to the Course Catalog
| Code | Course Title | Credit | Learning Time | Division | Degree | Grade | Note | Language | Availability |
|---|---|---|---|---|---|---|---|---|---|
| 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. | |||||||||
| GBE4010 | Drug Delivery Systems | 3 | 6 | Major | Bachelor/Master | Biomedical Engineering | Korean | Yes | |
| Drug delivery systems (DDS) is a system to deliver therapeutic or diagnostic agents to specific areas of action in the body in a spatiotemporal manner to effectively treat diseases without serious side effects. In this class, students will learn the fundamental concepts and principles of drug delivery technologies, and discuss current DDS applications and potentials future technologies. | |||||||||
| GBE4011 | Introduction to Human-level Artificial intelligence | 3 | 6 | Major | Bachelor/Master | 1-4 | Biomedical Engineering | - | No |
| This class will provide a synoptic level of introduction i) for the beneficial relationship between neuroscience and artificial intelligence, the two fields on which both industry and academia recently show great interests to study, and ii) for how they have been inspiring each other to pioneer their own research domain. Above all, the class will offer the students a bird-eye view for knowledge of biological and computational science through several representative examples, in order to implement human-level intelligence. | |||||||||
| GBE4013 | Deep learning with Python and Brain | 3 | 6 | Major | Bachelor/Master | 1-4 | Biomedical Engineering | - | No |
| In this course, students will learn the principles of deep learning. They will implement basic and popular neural network architectures and learn the similarity and differences between biological neural networks and artificial neural networks. Every week, they will do homework related to network training and interpret the training results. Students will have chances to experience how artificial intelligence and brain science interact with each other. | |||||||||
| GBE4014 | Special topics in computational neuroscience | 3 | 6 | Major | Bachelor/Master | 1-4 | Biomedical Engineering | English | Yes |
| This class aims to discuss particular topics from Fundamentals of Computational Neuroscience that are widely used in academia and industry. In addition, some topics that are not discussed in Fundamentals of Computational Neuroscience (e.g., information theory) will be discussed. For the last couple of classes, the important papers applying those theories will be discussed. | |||||||||
| GBE4015 | Advanced Biotechnology | 3 | 6 | Major | Bachelor/Master | 1-4 | Biomedical Engineering | - | No |
| Biotechnology and biomedical engineering technologies have been advancing rapidly and are driving the future development of various engineering fields. These technologies are actively applied in the fields of biomaterials, biosensors, drug delivery technologies and others.. In this class, we will introduce fundamentals of biotechnology and biomedical technologies and discuss the state-of-the-art future technologies. | |||||||||
| GBE4016 | Advanced Statistics and Applications | 3 | 6 | Major | Bachelor/Master | 1-4 | Biomedical Engineering | - | No |
| This course aims to introduce more complex mathematical models and analysis methods based on basic knowledge in probability and statistics, and to apply the learned concepts and models to various fields. In particular, focusing on analyzing neuroscience data as a major application, we will deal in detail with various statistical techniques specialized therein. The expected effect of the class will be to apply various statistical analysis methods and models suitable for diverse (big) data, thereby developing core competencies in the field of artificial intelligence and data science, which are rapidly increasing in demand. Prior to taking this course, the prerequisite that students should be familiar is as follows: random variables, conditional probabilities, sampling distribution, normal distribution, hypothesis testing, linear regression model, simple differential equations. It is also assumed that students have already taken calculus, probability, and statistics-related subjects (biostatistics and big data). The prospective students of this course are senior undergraduate and graduate students, and depending the number of enrolled students, the class will project-based to encourage students to actively participate in the class. The lectures focus on developing intuitions behind statistical models and techniques and their applications to neuroscience, rather than mathematical details. | |||||||||
| GBE4017 | Introduction to Computational Neuroscience | 3 | 6 | Major | Bachelor/Master | Biomedical Engineering | English | Yes | |
| Biological agents interact with the environment under certain operational principles at the level of individual neurons to behavior as a whole. The course will cover a wide range of computational approaches used in neuroscience to understand design principles at all levels. The course will have simple coding projects in Python or MATLAB, which will instantiate the concepts discussed in the class. Although the lecturer will focus on concepts and try to make the math-light class, having a linear algebra and calculus background will help in doing coding implementing mathmematical concepts for the projects. The course will be taught in English. | |||||||||
| GBE4018 | AI psychophysics | 3 | 6 | Major | Bachelor/Master | 1-4 | Biomedical Engineering | English | Yes |
| The main goal of this course is two-fold: on one hand, students are expected to get a better understanding of how the brain generates natural intelligence through lens of artificial intelligence (AI). On the other hand, students will attempt to reverse engineer the brain and build a better AI by mimicking how the brain instantiate natural intelligence. The following neural network models (deep learning) will be used in a variety of behavior domains. - Convolutional Neural Network: Image classification, numerical cognition - Recurrent Neural Network: perceptual decision making, interval timing - Transformer: relational inference - Reinforcement learning: value-based decision making After introducing fundamentals of the neural network models, students will participate in a hands-on project where they design a cognitive task and experiment for both human and AI, collect behavior data, and compare human with AI to gain insights about what is missing in the current AI and how AI can be improved to achieve human-level natural intelligence. The following books will be also covered in the class. - Summerfield, Christopher. Natural General Intelligence: How understanding the brain can help us build AI. Oxford University Press, 2022. - Lee, Daeyeol. Birth of intelligence: from RNA to artificial intelligence. Oxford University Press, 2020. | |||||||||
| GBE5042 | Electromagnetism in Biomedicine | 3 | 6 | Major | Master/Doctor | Biomedical Engineering | - | No | |
| This course targets GBME graduate students who want to acquire knowledge in fundamental classical electromagnetism for enhancing their research in biomedical devices. The course will cover classical theories of electromagnetic fields, scalar and vector potentials, and Maxwell's equations with a focus on biomedical applications. | |||||||||
| GBE7002 | Advanced Neurobiology Research MethodsⅠ | 3 | 6 | Major | Bachelor/Master/Doctor | Biomedical Engineering | - | No | |
| This class is an advanced class of neuroscience and neurobiology in graduate courses. This class discusses basic neurobiology and basic skills for carrying out graduate level researches. | |||||||||
| GBE7003 | Biomaterials Instrumental Analysis | 3 | 6 | Major | Bachelor/Master/Doctor | Biomedical Engineering | English | Yes | |
| This subject consists of mechanism, applications, managing of Analysis equipments in the field of BME for under graduate students. This subject is mainly focusing on how to use analytical instruments. | |||||||||
| GBE7004 | Introduction of Neurological Disease | 3 | 6 | Major | Bachelor/Master/Doctor | 1-8 | Biomedical Engineering | English | Yes |
| This class introduces basic knowledgements about neurological diseases. As we undergo dramatic advances and enhancement in technology and medicine, our average life expectation becomes extended and our society quickly turns into aging society. Therefore neuro-degenerative diseases prevail and many patients suffer from various types of neurological diseases. In order to develop therapeutic technology, it is important to understand basic mechanism of neurological diseases. This class delivers basic information about the underlying mechanism of various neurological diseases and discuss the possibility of developing new technology to treat them. | |||||||||
| GBE7005 | Principles of Magnetic Resonance Imaging | 3 | 6 | Major | Bachelor/Master/Doctor | 1-8 | Biomedical Engineering | - | No |
| Physical Principles of Magnetic Resonance Imaging (MRI) aims to provide an advanced understanding of essential principles and various applications of MRI from the physical and mathematical perspectives. Physical Principles of Magnetic Resonance Imaging 1 covers the principles of MR signal and image generation and essential concepts and theories of data acquisition and image reconstruction at an advanced level. | |||||||||