For more details on the courses, please refer to the Course Catalog
| Code | Course Title | Credit | Learning Time | Division | Degree | Grade | Note | Language | Availability |
|---|---|---|---|---|---|---|---|---|---|
| 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. | |||||||||
| GBE5005 | Advanced Biomedical Signal Processing | 3 | 6 | Major | Master/Doctor | 1-8 | Biomedical Engineering | - | No |
| Advanced Biomedical Digital Signal Processing aims to an advanced understanding of signal processing theories for analyzing real world digital signals. It covers digital signal processing including an optimal digital-filter design and mathematically derived algorithms for analysis of stochastic signals, spectral analyses, noise cancellation, and so on. | |||||||||
| GBE5006 | Brain Mapping Ⅰ | 3 | 6 | Major | Master/Doctor | 1-8 | Biomedical Engineering | - | No |
| This coursecovers magnetic resonance imaging-based brain mapping tools, focusing on functional MRI, diffusion tensor imaging, resting state fMRI, and perfusion imaging. Lecturesandliteraturereviewwillbecombined. Students will learn principles, methodologies, analyses, and sample applications, so that they are better equipped to understand the literature in MR-based neuroimaging and to conduct their own studies. | |||||||||
| GBE5009 | Advanced NeuroscienceⅡ for Biomedical Engineer | 3 | 6 | Major | Master/Doctor | 1-8 | Biomedical Engineering | - | No |
| This course is designed for graduate-level students. We will deliver basic knowledges for broad aspects of neurosciences. In addition, we will discuss recent advances in neuroscience. We will focus on system neuroscience, cognitive neuroscience. Prerequisite class for this class is Advanced Neuroscience I. | |||||||||
| GBE5010 | Advanced computational Neuroscience | 3 | 6 | Major | Master/Doctor | 1-8 | Biomedical Engineering | - | No |
| Computational neuroscience is the study that understands brain functions as information processes. It provides frameworks for understanding computations in single neural activities and neural population responses, through computer simulations. The class will overview computational models for single neuron, sensory processing, and motor processing. Special emphasis will be put on encoding and decoding models for sensory-motor transformation. | |||||||||
| GBE5012 | Advanced Neurotechnology | 3 | 6 | Major | Master/Doctor | 1-8 | Biomedical Engineering | - | No |
| This graduate course covers engineering technologies for neuroscience research in-depth. This course introduces cutting-edge topics in technical development for neuroscience, reviews seminal research outcomes, and discusses their significance and limitation. Furthermore, students propose their own ideas and write a research proposal. | |||||||||
| GBE5022 | Advanced Magnetic Resonance Imaging Ⅰ | 3 | 6 | Major | Master/Doctor | 1-8 | Biomedical Engineering | - | No |
| Advanced Magnetic Resonance Imaging (MRI) aims to provide an advanced understanding of MRI systems from the technological and mathematical perspectives. Hence, basic knowledge of MRI is a prerequisite for taking this class and we also recommend to first take the classes of Physical Principles of Magnetic Resonance Imaging 1, 2 we are providing at the graduate program. Advanced Magnetic Resonance Imaging 1 extensively covers the principles and applications of various RF pulses and gradients for a better understanding of their practical use and implementation. | |||||||||
| GBE5023 | Advanced Magnetic Resonance Imaging Ⅱ | 3 | 6 | Major | Master/Doctor | 1-8 | Biomedical Engineering | - | No |
| Advanced Magnetic Resonance Imaging (MRI) aims to provide an advanced understanding of MRI systems from the technological and mathematical perspectives. Hence, basic knowledge of MRI is a prerequisite for taking this class and we also recommend to first take the classes of Physical Principles of Magnetic Resonance Imaging 1, 2 we are providing at the graduate program. Advanced Magnetic Resonance Imaging 2 covers the principles and applications of data acquisition, image reconstructions, and a variety of pulse sequences at an advanced level. | |||||||||