For more details on the courses, please refer to the Course Catalog
| Code | Course Title | Credit | Learning Time | Division | Degree | Grade | Note | Language | Availability |
|---|---|---|---|---|---|---|---|---|---|
| FIT5017 | AI & Insurance Plan | 3 | 6 | Major | Master/Doctor | FinTech | - | No | |
| This is a course in AI & Insurance Plan for FinTech Master or Ph.D. program.This is a course in AI & Insurance Plan for FinTech Master or Ph.D. program.This is a course in AI & Insurance Plan for FinTech Master or Ph.D. program.This is a course in AI & Insurance Plan for FinTech Master or Ph.D. program.This is a course in AI & Insurance Plan for FinTech Master or Ph.D. program.This is a course in AI & Insurance Plan for FinTech Master or Ph.D. program.This is a course in AI & Insurance Plan for FinTech Master or Ph.D. program. | |||||||||
| ISS3222 | Introduction to Machine Learning | 3 | 6 | Major | Bachelor | - | No | ||
| Covers fundamental concepts for intelligent systems that autonomously learn to perform a task and improve with experience, including problem formulations (e.g., selecting input features and outputs) and learning frameworks (e.g., supervised vs. unsupervised), standard models, methods, computational tools, algorithms and modern techniques, as well as methodologies to evaluate learning ability and to automatically select optimal models. Applications to areas such as computer vision (e.g., characte r and digit recognition), natural language processing (e.g., spam filtering) and robotics (e.g., navigating complex environments) will motivate the coursework and material. | |||||||||
| ISS3224 | Data Visualization | 3 | 6 | Major | Bachelor | - | No | ||
| This course explores the field of data visualization. Topics cover the expanse of visualization from data preparation and cleaning to visualization types such as time series, box plots, and violin plots. Included in our study are visualization tools, online interactive visualizations, and other issues related to the display of big data. | |||||||||
| ISS3233 | Statistics in Python | 3 | 6 | Major | Bachelor | 1-4 | - | No | |
| This course will cover elementary topics in statistics using Python. The statistics topics include principles of sampling, descriptive statistics, binomial and normal distributions, sampling distributions, point and confidence interval estimation, hypothesis testing, two sample inference, linear regression, and categorical data analysis. Using Python, students will learn basic knowledge in Python programming, data management, data formats and types, statistical graphics and exploratory data analysis, and basic functions for statistical modeling and inference. | |||||||||
| ISS3290 | Introduction to Big Data Analysis | 3 | 6 | Major | Bachelor | - | No | ||
| Understand the genesis of Big Data Systems • Understand practical knowledge of Big Data Analysis using Hive, Pig, Sqoop • Provide the student with a detailed understanding of effective behavioral and technical techniques in Cloud Computing on Big Data • Demonstrate knowledge of Big Data in industry and its Architecture • Learn data analysis, modeling and visualization in Big Data systems | |||||||||
| MAE2007 | Analysis I | 3 | 6 | Major | Bachelor | 2-3 | Mathematics Education | Korean,Korean | Yes |
| The main contents are real and complex number systems, limits of sequences and functions, continuity and differentiability of functions, Riemann integrability of functions. | |||||||||
| MAE2008 | Analysis Ⅱ | 3 | 6 | Major | Bachelor | 2-3 | Mathematics Education | Korean | Yes |
| The main contents of this course are sequences and series of functions, uniform convergence, differnetiation and integration of functions of several variabels, implicit function theorem, inverse function theorem, metric spaces. | |||||||||
| MAE3013 | Real Analysis | 3 | 6 | Major | Bachelor | 4 | Mathematics Education | Korean | Yes |
| In this course, we study the Lebesque measure and theory of integration. -Measure Theory and Integration : albebra, sigma-algebra, measure, integration, convergece, L^p-space | |||||||||
| SOE3001 | Statistical Learning and Artificial Intelligence | 3 | 6 | Major | Bachelor | 3-4 | Economics | Korean | Yes |
| This course covers a variety of topics related to statistical learning and artificial intelligence. Students will learn concepts and basic theories of various statistical methods and models used in artificial intelligence. In order to cultivate analytical skills and problem-solving skills for real data, Python program and deep learning package are used to practice real data. | |||||||||
| STA2008 | Mathematics for Statistics | 3 | 6 | Major | Bachelor | 2-3 | Korean,Korean | Yes | |
| Basic mathematical tools needed to learn statistical theories. This course includes basic manipulation of vectors and matrices, eigenvectors. Existing methods solve typical linear equations, etc. This course also will include differentiation and integration. Especially, partial differentiation, several rules useful in differentiation, and multiple integration methods, and special functions. | |||||||||
| STA2009 | Introduction to Statistical Computing | 3 | 6 | Major | Bachelor | 2-3 | - | No | |
| This course introduces students to a range of computational techniques that are important to statistics. The topics covered include numerical linear algebra, numerical optimization, graphical techniques, numerical approximations, numerical integration and Monte Carlo methods. Use of statistical packages (R, SAS) and programming libraries is also illustrated. | |||||||||
| STA2010 | Introduction to Regression Analysis | 3 | 6 | Major | Bachelor | 2-3 | English,Korean | Yes | |
| Introduction to basics of linear regression models. Topics covered include simple linear regression, ordinary least squares, the geometry of least squares, F tests and ANOVA table, residuals, outlier detection, and identification of influential observations, etc. | |||||||||
| STA2011 | Statistics | 3 | 6 | Major | Bachelor | 2-3 | Korean,Korean | Yes | |
| Introduction to statistical concepts and methods for the collection, presentation, analysis, and interpretation of data. Histograms, means, standard deviations, medians as descriptive and summary statistics, and several important distributions including binomial and normal distributions. This course will concentrate on statistical inferences based on the knowledge from Introduction to Statistics. Basic concepts of estimation, testing and, test efficiency. Introduction to regression and analysis of variance will be covered | |||||||||
| STA2014 | Introduction to Mathematical Statistics | 3 | 6 | Major | Bachelor | 2-3 | English | Yes | |
| Topics include the concept of random variable and several statistical probability functions. Characteristics and relationships among the functions will be studied to apply to real world. Also random sample and distribution of sample mean will be explained | |||||||||
| STA2016 | Introduction to statistical programming | 3 | 6 | Major | Bachelor | 2-3 | English | Yes | |
| This course introduces basic logic and grammars for computer programming based on the statistical work and programming environment R which is the most widely used statistical language for professional statisticians. The first part of the course gives an introduction to R. In this stage, students learns how to assign variables, to import and export data, to handle datatypes, to use loop statements, and to write user-defined functions. In the second part, this course provides a brief review for some basic statistical theory and methods to compute basic statistics. In addition, students learn some important numerical methods for optimization, differentiation, and integration. | |||||||||