Introductory AI courses

Advanced Algorithm

This course offers an intensive learning opportunity about effective algorithm design and analysis for those who have completed algorithm courses during undergraduate programs. It covers such topics as graph algorithm, algebraic algorithm, string algorithm, geometric algorithm, and approximate algorithm.

Data Statistics

This course is designed to give students an opportunity to learn about the concept of basic statistical techniques for actual data analysis and problem-solving. In this course, students will learn about advanced statistics, including regression analysis and multivariate data analysis, as well as a method to plan an experiment.

Deep Learning Theory and Practice

The basics of deep learning theory are taught very simply, and mainly the latest theories are covered. Lectures on the latest CNN structures such as Efficientnet as well as deep learning architectures that have been actively researched in the deep learning field for the past 3 years such as Transformer, BERT, and Vision Transformer will be given. Also, the attention modules, augmentation technologies, and the latest deep learning algorithms will be covered. This course includes effective practice so that students can directly implement the deep learning structures and algorithms introduced in theory lectures. Students first implement the most basic deep learning structure that can be a baseline, and then apply the latest deep learning structure and learning algorithm learned through theory classes to confirm that the performance is improved. Through this, it induces active participation of students and enables effective theoretical and practical learning.

Software Requirements Engineering

This course aims to acquire the in-depth knowledge necessary for software requirements analysis and design. In addition to a brief overview of SW engineering, students will learn SW requirements analysis and design techniques in SW development methodology. In addition, the SW development target is presented as a problem, and students are encouraged to take the initiative in the requirements analysis and design process for SW development.

Theory of Statistics

This class deals with statistical estimation and hypothesis testing theory among statistical theories that have not been dealt with in probability and statistics and mathematical statistics courses. Families of distributions and sufficiency are covered, and the minimum error estimator and maximum likelihood estimator will also be covered. The most powerful testing theory and the unbiased testing theory are studied.

Human-Centered Artificial Intelligence

This course introduces the basics of artificial intelligence as well as interpretable artificial intelligence methods. In the introduction of artificial intelligence, students will learn about the overall relation and form of artificial intelligence methods, decision making processes-state search based method, constraint-based method, probabilistic reasoning, and so on. With a focus on the interpretability of data-based optimization methods, this course introduces a variety of tree-based classification methods, regression solvers and rule-based methods.

Computer Programming for AI&Data Processing

This course is designed to provide an opportunity to learn programming skills required for processing data by using a computer system. Students will learn about the basic concepts of variable, type, condition, iteration and function for programming and how to apply them to a variety of computer programming languages including Python, C++ and JavaScript. As learning achievements, students can understand the basic concept of programming and develop a simple program, and they can learn a method to process data by writing a program.

Entrepreneurship in Artificial Intelligence

This class will cover the following contents. (1) Identify the necessary competencies as an entrepreneur and learn basic knowledge related to them. (2) Explore the process required for startups, especially Lean startups. (3) A representative start-up in the field of artificial intelligence is discovered, and each case is presented and discussed based on the research conducted by the students. (4) Each student devises a business idea based on artificial intelligence and then pitches the rough business plan through class time. (5) Validate the feasibility of the idea implementation using the publicly available AI learning data (AI Hub), and conduct a “demo” along with the announcement of the business plan.

Statistical Data Analysis I

This class deals with statistical theories and applied techniques applied to real cases (medicine, engineering, finance, environment, etc.) from the perspective of categorical data analysis. The topics include generalized linear models including loglinear models, logit models, logistic regression models, GEE models, and repeated categorical data analysis using random effects.

Statistical Data Analysis II

As a follow-up course to Statistical Data Analysis I, statistics applied to real cases such as regression model, survival model, time-series model, generalized linear model, multivariate linear model, and repeated measurement data analysis including random effects will be covered from the perspective of continuous data analysis.

Fundamental AI courses

Advanced machine learning

This course provides top-class lectures about data mining and machine learning and introduces various applicable techniques including both classical methodologies and the latest learning algorithms. This course also covers a variety of classification methods, high-dimensional regression models, clustering, bagging and boosting, factor analysis, hidden Markov model, probabilistic graphical model, and so on.

Advanced Artificial Intelligence

In this course, students learn intensively about knowledge presentation and inference. Especially, it offers an opportunity to learn about knowledge presentation and inference for ontology engineering and to conduct an in-depth analysis of related case studies.

Advanced Human-Computer Interaction

This course introduces HCI models, theories and frameworks necessary to conduct research in HCI field and gives an opportunity to investigate the latest research trend in HCI field. Also, this provides an opportunity to learn about methodologies and skills applicable to solve actual problems in a variety of HCI application fields, including social computing, human computation, machine learning, visualization, and mobile Interaction.

Advanced Computer Vision

Humans perceive the three-dimensional structure of the world with apparent ease. The goal of a computer vision is to achieve the dream of having a computer interpret an image at the same level. In this course, we will explores the variety of techniques commonly used to analyze and interpret images. It also describes challenging real-world applications where vision is being successfully used, both for specialized applications such as medical imaging, and for fun, consumer-level task such as image editing and stitching, which students can apply to their own personal photos and videos. Moreover, we will study the deep learning based computer vision methods from common CNN-based object recognition to RNN-based sequential image processing. To handle this latest method, we will study the deep learning tools such as caffe, torch and tensor flow and from AlexNet to ResNet from the viewpoint of computer vision application.

Advanced Data Mining

Data capture has become inexpensive and ubiquitous as a by-product of innovations such as the internet, e-commerce, electronic banking, point-of-sale devices, bar-code readers, intelligent machines, and the amount has been increasing at an incredible rate due to technological advances. “Data mining” refers to a collection of techniques for extracting “interesting” relationships and knowledge hidden in a mountain of data in order to assist managers or analysts in making intelligent use of them. A number of successful applications have been reported in areas such as credit rating, fraud detection, database marketing, customer relationship management, and stock market investments. In this course, we will examine a variety of data mining techniques evolved from the disciplines of statistics and artificial intelligence (or machine learning), and practice them in recognizing patterns and making predictions from an applications perspective. Application (or case) surveys and hands-on experimentations with easy-to-use software will be provided.

Advanced Image Signal Processing

This course introduces various image signal processing techniques. This course covers linear processing (image enhancement and video playback), non-linear processing (watershed transformation, morphology), color image processing (edge detection by color slope), multi-dimensional image processing, etc. It deals with major existing image processing techniques (image segmentation, multidimensional image classification, with greater focus on practice computing sessions rather than through theoretical ones.

Advanced Software Engineering

This course provides a top-class lesson about software engineering. The concept, methodology, and technique of the existing software engineering were analyzed and evaluated to overcome its limitations and constraints. As a result, new concepts and methodologies have emerged. The concept, methodology and technique of object-oriented software engineering (OOSE), system engineering, component-based software engineering (Component Based S.E.), and architecture-based software engineering will be comprehensively examined to identify and evaluate their applicability in a real environment and to offer an insight into how this field is going to develop in future.

Advanced Database

This course gives an opportunity to understand the latest research issues in the field of database. To be specific, students will learn about object-oriented databases, object-relational database, XML database, multimedia databases, next generation flash memory-based database, and so on.

Advanced Operating System

This course will give students an opportunity to learn about the structure and implementation of Linux operating system. To be specific, they will learn about loader, shell programming, etc. and Linux's major data structure, module management, VFS, device drivers, network-related modules, and the techniques of implementing device drivers and major system calls.

Advanced Computer Architecture

To improve the function of a high-performance processor design, Instruction Level Parallelism (ILP), Thread Level Parallelism (TLP), multi-core techniques, and parallel computers are being used recently. These technologies were mainly used for personal computers but are now being applied to smartphones and smart pads. This technology and market change will lead us to explore a new domain of microprocessor design in future. Under the theme of advanced computer structure, this course offers an opportunity to learn about adaptive dynamic branch prediction, high bandwidth instruction fetch, dynamic instruction scheduling, Tomasulo algorithm, superscalar, speculation, multi-threading, symmetric multiprocessors, shared memory multiprocessors, cache and memory hierarchy design, and so on.

Distributed and Parallel Programming

Distributed and parallel programming can perform multiple tasks or jobs simultaneously to provide a solution to a large-scale computing problem and is used to provide high performance computing & high throughput computing. Due to the recent exponential growth of data (Big Data) together with the emergence of multicore and manycore (GPGPU) and the expansion of MapReduce programming model, there is again a growing need for distributed and parallel programming. Thus, this course gives an opportunity to learn about the theory and applications of distributed and parallel programming. In this course, students will learn about platforms and models, which are the foundations of distributed and parallel programming, as well as MPI, which is a parallel programming tool based on traditional high-performance computing cluster. They will also learn and about parallel computing using GPGPU, such as MapReduce (Hadoop) and CUDA (PyCUDA), which are now grabbing attention in relation to cloud computing and big data.

Theory and Applications of Reinforcement Learning

This course gives an opportunity to learn about general algorithms of computer vision. Computer vision is about analyzing a three-dimensional environment from a still image or video and building a 3D modeling. This course introduces basic concepts of image filtering and sampling, and students will learn about the representative algorithm of each field of computer vision, including edge detection, projection, image matching, motion estimation, image segmentation, and also about their mathematical models. At the end of the semester, each student has to perform a project to implement and improve computer vision algorithms proposed by recent related research articles in order to acquire know-how about computer vision.

Advanced AI Courses

Theory and Applications of Reinforcement Learning

This course provides theoretical details by introducing basic knowledge about multi-armed bandit, Markov decision process, Monte-Carlo method, Q-learning, value function approximation, policy gradient, and deep Q-learning network. In addition, students will review various application cases and participate in a project to apply theories to research.

Advanced Information Security

The course aims to help students to understand advanced theories of information security. First, they will develop an understanding of the meaning, importance and goal of information protection and also learn about advanced theories related to information protection, including cryptography, security model and policy, operating system security, program security, malicious code, and security assessment and management.

Advanced Machine Learning Theory

Machine learning is all about finding generalized patterns from data. The whole idea is to replace the “human writing code” with a “human supplying data” and then let the system figure out what it is that the person wants to do by looking at the examples. In recent years, many successful applications of machine learning have been developed, ranging from data-mining programs that learn to detect fraudulent credit card transactions, to autonomous vehicles that learn to drive on public highways. At the same time, there have been important advances in the theory and algorithms that form the foundation of this field. The goal of this class is to provide an overview of the state-of-art algorithms used in machine learning and different perspectives, and hopefully to gain some understanding of what’s going on the next. We will discuss both the theoretical properties of these algorithms and their practical applications.

Technology Intelligence

'Technology Intelligence' means a process of identifying opportunities and threats through collection, integration, analysis and visualization of technology information and then providing the information to decision makers. This course provides theoretical lectures about patents and trademark rights, the most representative sources of technical information, and students will conduct analysis by using them alone or integrating them with other information sources, including businesses, profiles and web data to help them develop an ability to identify the trend of technology and competitors and ultimately to support the decision-making of an organization. Also, this course will invite outside lecturers including patent attorneys in order to produce researchers equipped with practical skills and theories.

Technology forecasting

In order to stay competitive, businesses have to monitor the development of current technology in a rapidly changing science and technology environment and need to find a newly emerging promising technology. And efforts continue to be made at a national level to predict a changing direction of future society and technology and to develop a promising research and technology that will lead the future. In this course, students will examine various methodologies to predict future technology, learn about the advantage and disadvantage of each methodology, and review application cases.

Social Media Analysis

- Social media data collection and storage using API and through web crawling
- Data pre-processing and compression, and analysis using various methods for correlation, regression, and classification
- Linguistic characteristics analysis and sentiment analysis
- Social media data analysis and visualization using various tools suitable to research purposes

Natural Language Processing

In this course, students will learn about a series of processes including pre-processing and datafication of various types of information expressed in natural language, and application of different analysis methods to extract meaningful information. Especially they will develop an ability to analyze and utilize the latest research trend in related fields.

Computational Biology

In this course, students will learn basic knowledge, application, prospect of computational biology, which is a convergence field of combining BT and IT. This course provides a brief introduction of molecular biology and R programming, and students will learn about biomedical sciences, including sequence analysis, disease association analysis, gene expression analysis, and systems biology. This course deals with various types of data analysis techniques for clustering, classification, timeseries data analysis, and network mining.

Information Retrieval

In this course, students will learn about information retrieval models, Boolean model, vector analysis model, and research models based on cognitive science model. Based on research literatures, they will learn about technologies related to internet search, index extraction, filtering, clustering, and concept-based retrieval. For application of the related technologies, students will perform projects to develop systems for retrieving information from the Internet, and also conduct tasks to implement technologies for each project.

Cloud Computing

Cloud computing is the most important paradigm in the current IT environment, and many researchers expect that more efficient and better-performing resources can be provided through cloud computing and they go further to predict that a new type of service and application (application system) can be provided by cloud computing. This on-demand based computing paradigm requires a variety of computing technologies. This course offers an opportunity to learn about these technologies and application systems that use cloud computing paradigms. The course deals with detailed topics, including the introduction of cloud computing, system model, virtualization technology, cloud platforms, cloud programming environments, and SOA.

Pattern Recognition Theory

In this course, students will learn about pattern recognition methods. First, students will learn the concept of unsupervised learning and supervising learning, as well as their differences. They will also learn the difference between classification and regression, which are categorized under the same umbrella of supervised learning. The course deals with the representative algorithm of each method and their mathematical modeling. At the end of the semester, they will perform end-of-semester projects including implementation of a face recognition system to acquire know-how about pattern recognition.

AI Convergence Courses

Global Internship 1/2/3

Advanced Topics in Machine Learning 1

This course deals with the latest theories, applications and trend of machine learning. Especially, this course introduces the latest related research trend and allows them to have a discussion to help them develop logical reasoning and debating skills.

Advanced Topics in Machine Learning 2

This course deals with the latest theories, applications and trend of machine learning. Especially, this course introduces the latest related research trend and allows them to have a discussion to help them develop logical reasoning and debating skills.

Open AI Special Lecture 1

The course offers an opportunity to learn about methodologies of applying theoretical knowledge learned from machine learning and deep learning to big data and real network problems and also to perform a creative research by applying these methodologies to a new application field.

Open AI Special Lecture 2

The course offers an opportunity to learn about methodologies of applying theoretical knowledge learned from machine learning and deep learning to big data and real network problems and also to perform a creative research by applying these methodologies to a new application field.

Industrial Mathematics Survey 1

In this course, students will learn about specific problems in the industry that requires mathematics and explore applicable mathematical tools to provide a solution to the problems, and also will practice how to write a report and deliver an oral presentation by participating in a team project.

Internship 1/2/3

In this course, students will develop nurture practical skills by participating in internship programs at ICT-related industrial companies or research institutes