For more details on the courses, please refer to the Course Catalog
| Code | Course Title | Credit | Learning Time | Division | Degree | Grade | Note | Language | Availability |
|---|---|---|---|---|---|---|---|---|---|
| MTH4034 | Advanced Mathematical Finance | 3 | 6 | Major | Bachelor/Master | 1-2 | - | No | |
| This is an introduction to the graduate level follow-up course for Mathematical Finance which covers the material including PDE connection to Financial Mathematics, Exotic Options, American Derivative Securities, Change of Numeraire, Term-Structure Models and Jump Processes. | |||||||||
| MTH4034 | Advanced Mathematical Finance | 3 | 6 | Major | Bachelor/Master | 1-2 | Mathematics | - | No |
| This is an introduction to the graduate level follow-up course for Mathematical Finance which covers the material including PDE connection to Financial Mathematics, Exotic Options, American Derivative Securities, Change of Numeraire, Term-Structure Models and Jump Processes. | |||||||||
| MTH4035 | Mathematics of Data science and Machine learning | 3 | 6 | Major | Bachelor/Master | - | No | ||
| Based on basic mathematics such as linear algebra, probability/statistics, and multivariable calculus, we study basic principles and methods in data science and machine learning. Furthermore, we also examine relations with diverse fields of mathematics, differential geometry, topology, real analysis, etc. We will heavily practice machine learning and big data related computer languages and tools, mainly Python/scikit-learn and R. | |||||||||
| MTH4035 | Mathematics of Data science and Machine learning | 3 | 6 | Major | Bachelor/Master | Mathematics | - | No | |
| Based on basic mathematics such as linear algebra, probability/statistics, and multivariable calculus, we study basic principles and methods in data science and machine learning. Furthermore, we also examine relations with diverse fields of mathematics, differential geometry, topology, real analysis, etc. We will heavily practice machine learning and big data related computer languages and tools, mainly Python/scikit-learn and R. | |||||||||
| MTH4036 | Mathematical Principle and Programming of Deep Learning | 3 | 6 | Major | Bachelor/Master | 1-4 | - | No | |
| Deep learning is a technique that combines a variety of machine learning techniques and deep neural networks, and is importantly used in various industrial fields such as image classification, speech recognition, and advanced control. This course is an introductory course to deep learning, and students will develop the ability to understand and program the mathematical principles of deep learning. In the previous part, we learn and implement the basics of Python programming by studying some basic machine learning problems. Based on this, we understand the mathematical principle of deep learning, derive the backpropagation formula of the optimization model, and implement it in Python programming. | |||||||||
| MTH4036 | Mathematical Principle and Programming of Deep Learning | 3 | 6 | Major | Bachelor/Master | 1-4 | Mathematics | - | No |
| Deep learning is a technique that combines a variety of machine learning techniques and deep neural networks, and is importantly used in various industrial fields such as image classification, speech recognition, and advanced control. This course is an introductory course to deep learning, and students will develop the ability to understand and program the mathematical principles of deep learning. In the previous part, we learn and implement the basics of Python programming by studying some basic machine learning problems. Based on this, we understand the mathematical principle of deep learning, derive the backpropagation formula of the optimization model, and implement it in Python programming. | |||||||||
| MTH4037 | Matrix Lie Groups | 3 | 6 | Major | Bachelor/Master | Korean | Yes | ||
| Lie groups play an enormous role in modern geometry on several different levels and in geometric topology, functional analysis, number theory, and also in probability and applied mathematics. In this course, we introduce Lie theory based on very basic knowledge in linear algebra/matrix analysis(matrix factorization, matrix manifolds, matrix optimization, eigenvalue analysis). | |||||||||
| MTH4037 | Matrix Lie Groups | 3 | 6 | Major | Bachelor/Master | Mathematics | Korean | Yes | |
| Lie groups play an enormous role in modern geometry on several different levels and in geometric topology, functional analysis, number theory, and also in probability and applied mathematics. In this course, we introduce Lie theory based on very basic knowledge in linear algebra/matrix analysis(matrix factorization, matrix manifolds, matrix optimization, eigenvalue analysis). | |||||||||
| MTH4038 | Optimization theory and computation | 3 | 6 | Major | Bachelor/Master | Korean | Yes | ||
| Optimization aims to find the minimum of a given function. Specifically, it includes the process of designing and programming an algorithm so that the computer can find the point with the minimum value of the function through iterative calculations. Such optimization appears in a wide variety of fields such as natural science, engineering, and social science, and has recently attracted more attention as very large-scale optimization problems have emerged in the field of artificial intelligence. In this class, we learn the theory and programming methods of optimization algorithms. | |||||||||
| MTH4038 | Optimization theory and computation | 3 | 6 | Major | Bachelor/Master | Mathematics | Korean | Yes | |
| Optimization aims to find the minimum of a given function. Specifically, it includes the process of designing and programming an algorithm so that the computer can find the point with the minimum value of the function through iterative calculations. Such optimization appears in a wide variety of fields such as natural science, engineering, and social science, and has recently attracted more attention as very large-scale optimization problems have emerged in the field of artificial intelligence. In this class, we learn the theory and programming methods of optimization algorithms. | |||||||||
| MTH4039 | An Introduction to Algebraic Graph Theory | 3 | 6 | Major | Bachelor/Master | Korean | Yes | ||
| One of the important branches of Mathematics is Graph theory. Algebraic graph theory is a branch of Mathematics that studies graphs by using algebraic properties. In this course we will learn various topics in algebraic graph theory. The topics covered in this course include introduction of graphs degree sequence of graphs, Eccentricity of graphs, Graph colorings, regular graphs, adjacencymatrix of graphs, and Laplacian matrix of graphs. The aim is to translate properties of graphs into algebraic properties and then using the results and methods of algebra, to deduce theorems about graphs. | |||||||||
| MTH4039 | An Introduction to Algebraic Graph Theory | 3 | 6 | Major | Bachelor/Master | Mathematics | Korean | Yes | |
| One of the important branches of Mathematics is Graph theory. Algebraic graph theory is a branch of Mathematics that studies graphs by using algebraic properties. In this course we will learn various topics in algebraic graph theory. The topics covered in this course include introduction of graphs degree sequence of graphs, Eccentricity of graphs, Graph colorings, regular graphs, adjacencymatrix of graphs, and Laplacian matrix of graphs. The aim is to translate properties of graphs into algebraic properties and then using the results and methods of algebra, to deduce theorems about graphs. | |||||||||
| MTH4040 | Probabilistic Generative Model | 3 | 6 | Major | Bachelor/Master | Korean | Yes | ||
| We study various approaches and theoretical backgrounds of probabilistic generative models, and explore how they are utilized in the process of data generation. This includes examples such as Generative Adversarial Networks (GANs), Variational Autoencoders (VAEs), Normalizing Flows, and Diffusion Models. To support this, statistical methodologies related to generative models, such as Markov chains, variational inference, and Monte-Carlo simulation, are also taught. Additionally, the course may include the latest approaches for probabilistic generative models. | |||||||||
| MTH4040 | Probabilistic Generative Model | 3 | 6 | Major | Bachelor/Master | Mathematics | Korean | Yes | |
| We study various approaches and theoretical backgrounds of probabilistic generative models, and explore how they are utilized in the process of data generation. This includes examples such as Generative Adversarial Networks (GANs), Variational Autoencoders (VAEs), Normalizing Flows, and Diffusion Models. To support this, statistical methodologies related to generative models, such as Markov chains, variational inference, and Monte-Carlo simulation, are also taught. Additionally, the course may include the latest approaches for probabilistic generative models. | |||||||||
| MTH5001 | Research Course I | 3 | 0 | Major | Master/Doctor | 1-4 | Korean | Yes | |
| Independent study for the advanced research. | |||||||||