For more details on the courses, please refer to the Course Catalog
| Code | Course Title | Credit | Learning Time | Division | Degree | Grade | Note | Language | Availability |
|---|---|---|---|---|---|---|---|---|---|
| ADS5001 | Basic of Datascience | 3 | 6 | Major | Master/Doctor | 1-8 | - | No | |
| This course introduces concepts, issues and problems of Big Data. This course covers issues such as data storage, mining, analysis, visualization, and application in various domains. In addition, you will learn about tools, algorithms, and platforms for dealing with Bigdata. Students gain practical knowledge through field projects or case studies in the fields of business, engineering, sociology and life sciences. | |||||||||
| ADS5002 | Basic Statistics | 3 | 6 | Major | Master/Doctor | 1-8 | Korean | Yes | |
| In this course you will learn how to program in R and how to use R for effective data analysis. You will learn how to install and configure software necessary for a statistical programming environment, discuss generic programming language concepts as they are implemented in a high-level statistical language. The course covers practical issues in statistical computing which includes programming in R, reading data into R, accessing R packages, writing R functions, debugging, and organizing and commenting R code. | |||||||||
| ADS5003 | Bigdata Processing | 3 | 6 | Major | Master/Doctor | 1-8 | Korean | Yes | |
| Bigdata system is similar to legacy database system on interface but quite different usually. This course covers necessary techniques for naturally Bigdata processing with consider of Bigdata. Students will understand to Bigdata system and how to handle Bigdata in the system. Students learn Bigdata store, load, process, integration, policy on Hadoop system environment. | |||||||||
| ADS5004 | Data Analysis Language | 3 | 6 | Major | Master/Doctor | 1-8 | Korean | Yes | |
| This course provides students with opportunities to develop skills and solve statistical problems using Python and R. Students learn about Python programs and how to use them for efficient data analysis. Understand the software installation and settings required in statistical programming environment, and general programming concepts. This course emphasizes data processing and basic statistical analysis. This course requires basic knowledge of basic statistics and does not require prior experience in basic computer programming. | |||||||||
| ADS5005 | Multivariate Statistics | 3 | 6 | Major | Master/Doctor | 1-8 | Korean | Yes | |
| An introduction to multivariate statistical models, well balancing three equally important elements: the mathematical theory, applications to real data, and computational techniques. Traditional multivariate models and their recent generalizations to tackle regression, data reduction and dimensionality reduction, classification, predictor and classifier instability problems. | |||||||||
| ADS5006 | Advanced Machine Learning | 3 | 6 | Major | Master/Doctor | 1-8 | Korean | Yes | |
| This course provides a broad introduction to machine learning, datamining, and statistical pattern recognition. Topics include: (i) Supervised learning (parametric/non-parametric algorithms, support vector machines, kernels, neural networks). (ii) Unsupervised learning (clustering, dimensionality reduction, recommender systems, deep learning). (iii) Best practices in machine learning (bias/variance theory; innovation process in machine learning and AI). The course will also draw from numerous case studies and applications, so that you'll also learn how to apply learning algorithms to building smart robots (perception, control), text understanding (web search, anti-spam), computer vision, medical informatics, audio, database mining, and other areas. | |||||||||
| ADS5007 | Artificial Intelligence | 3 | 6 | Major | Master/Doctor | 1-8 | - | No | |
| The course objectives are to learn techniques and theory developed in major areas of Artificial Intelligence. The topics are: problem solving and search, logic and knowledge representation, reasoning, machine learning, soft computing, data mining and miscellaneous topics in the current research. | |||||||||
| ADS5010 | Application of Linear Algebra | 3 | 6 | Major | Master/Doctor | 1-8 | Korean | Yes | |
| Linear Algebra is the study of vector spaces and linear transformations on vector spaces. Techniques from Linear Algebra are also used in analytic geometry, engineering, physics, natural science, computer science, and the social sciences. Topics include the use and application of matrices in the solution of systems of linear equations, determinants, real n-dimensional vector spaces, abstract vector spaces and their axioms, linear independence, span and bases for vector spaces, linear transformations, eigenvalues and eigenvectors, matrix factorizations, and orthogonality. Computer explorations using MATLAB is an integral component of this course. | |||||||||
| ADS5011 | Web Mining | 3 | 6 | Major | Master/Doctor | 1-8 | - | No | |
| Web mining refers to the automatic discovery of interesting and useful patterns from the data associated with the usage, content, and the linkage structure of Web resources. It has quickly become one of the most popular areas in computing and information systems because of its direct applications in e-commerce, e-CRM, Web analytics, information retrieval/filtering, Web personalization, and recommender systems. The primary focus of this course is on Web usage mining and its applications to e-commerce and business intelligence. Specifically, we will consider techniques from machine learning, data mining, text mining, and databases to extract useful knowledge from Web data which could be used for site management, automatic personalization, recommendation, and user profiling. | |||||||||
| ADS5012 | Data Modeling | 3 | 6 | Major | Master/Doctor | 1-8 | - | No | |
| This course forms an important basis solve practical statistics and data analysis related problems arising in broad computer science and engineering, and daily life. Students will have gained a solid understanding of probabilistic data modeling, interpretation, and analysis. | |||||||||
| ADS5013 | Advanced in Database System | 3 | 6 | Major | Master/Doctor | 1-8 | Korean | Yes | |
| From this course, students learn fundamental concept and theories of data management system (DBMS). This course introduces principal technique of DBMS, data load, external sort, tree indexing, hash indexing, query optimization, physical design and tuning, transaction, concurrency control, recovery techniques. | |||||||||
| ADS5014 | Advanced in Bigdata Platform | 3 | 6 | Major | Master/Doctor | 1-8 | - | No | |
| This course covers Hadoop and Hadoop Eco System which is a group of applications based on and working with Hadoop. Students learn Hadoop architecture, software stack and principle of its processes like map-reduce. Students study Hadoop eco system, like Hive, hbase, Spark, scoop, flume, kafka, Azkaban, ambari, etc. | |||||||||
| ADS5015 | Advanced in Data Visualizing | 3 | 6 | Major | Master/Doctor | 1-8 | - | No | |
| This course is an introduction to key design principles and techniques for visualizing data. The major goals of this course are to understand how visual representations can help in the analysis and understanding of complex data, how to design effective visualizations, and how to create your own interactive visualizations. The main contents are the basics of visualization such as 2D vector graphics, graphics programming, charts, graphs and animation, relational visualization, visualization of information such as text and DB. | |||||||||
| ADS5016 | Natural Language Processing | 3 | 6 | Major | Master/Doctor | 1-8 | Korean | Yes | |
| Natural language processing (NLP) is one of the most important technologies of the information age. Understanding complex language utterances is also a crucial part of artificial intelligence. There are a large variety of underlying tasks and machine learning models behind NLP applications. In this course students will learn to implement, train, debug, visualize and invent their own neural network models. The course provides a thorough introduction to cutting-edge research in deep learning applied to NLP. this course will cover word vector representations, window-based neural networks, recurrent neural networks, long-short-term-memory models, recursive neural networks, convolutional neural networks as well as some recent models involving a memory component. | |||||||||
| ADS5017 | Optimization | 3 | 6 | Major | Master/Doctor | 1-8 | - | No | |
| This course introduces linear and nonlinear programming, iterative and dynamic programming, especially for optimal control problems. Discrete and continuous optimal regulators are derived from dynamic programming approach which also leads to the Hamilton-Jacobi-Bellman Equation and the Minimum Principle. Minimum energy problems, linear tracking problems, output regulators and minimum time problems are considered. | |||||||||