The Global Leader, SKKU

Search
Close
search
 

Academic Programs

  • home
  • Academic Programs
  • Schools/Departments
  • Applied Artificial Intelligence (Special Graduate)
  • Course&Curriculum

Applied Artificial Intelligence (Special Graduate)

For more details on the courses, please refer to the Course Catalog

교육과정
Code Course Title Credit Learning Time Division Degree Grade Note Language Availability
AAID001 Understanding AI and basics in data science 3 6 Major Master 1-5 Korean Yes
In this lecture, you will learn the core technologies of artificial intelligence, and identify cases where related technologies are currently used in real industries. In addition, the basic concepts of artificial intelligence technology will be explored. Further, various methods of exploration and optimization, knowledge representation and inference techniques, machine learning methods including deep learning, and planning methods will be outlined.
AAID002 Programming basics for applied AI education 3 6 Major Master 1-5 Korean Yes
In this lecture, you will learn the basics of programming for convergence education in the AI era. Students taking this course aim to develop the ability to perform basic coding tasks using the Python programming language. Also, Student will create AI convergence education program using learned programming skills.
AAID003 Data structure and algorithm for data science 3 6 Major Master 1-5 Korean Yes
This is a basic course for problem solving in the field of data science and deals with the analysis of given problems, design of data structures, algorithm development, and implementation techniques.
AAID004 Computing thinking and problem solving 3 6 Major Master 1-5 Korean Yes
In this course, students learn how to find problems in a given environment using special thought patterns and procedures, solve them, or devise programs for necessary computing. Specifically, this course deals with the concepts of problem decomposition, pattern matching, abstraction, and automation, and aims to apply what students have learned to real situations.
AAID005 Special Issues on Machine Learning and Deep Learning 3 6 Major Master 1-5 Korean Yes
This course starts with the history of machine learning and deep learning, and learns the concepts and technical terms of each model. In addition, by learning about the latest artificial intelligence neural network models such as CNN, RNN, GAN, and LSTN based on artificial intelligence currently applied, the students learn the theoretical foundation.
AAID006 AI Ethics 3 6 Major Master 1-5 - No
Today, the development of artificial intelligence provides a lot of convenience to our lives, but there is a controversy over whether artificial intelligence used in various fields will damage human dignity or ethics. The AI ​​Ethics course deals with the development of artificial intelligence as well as appropriate behaviors, mindsets, and social issues that humans must have.
AAID007 Virtual Reality Video Processing 3 6 Major Master - No
This lecture provides the technologies for most recent virtual reality(VR) services. It covers the understanding (1) the international video standards such as MPEG-immersive, (2) the 360-degree video and metadata processing, and (3) the multimedia computing systems for the VR. It also provides the exercises and experiments using standard reference SW, test model for immersive video(TMIV), for understanding overall VR processing systems.
AAID008 Analysis of Learning Materials and Methods of AI Convergence Education 3 6 Major Master - No
“Analysis of Learning Materials and Teaching Methods of AI Convergence Education” is a course for developing AI convergence education course materials at an appropriate level and acquiring teaching methods suitable for learners. In this course, students learn the process of researching, developing, and evaluating various textbooks for classes that integrate artificial intelligence (AI) not only to core subjects, such as Mathematics, Science, and Information Technology, but also to Humanities and Social Sciences as well as the Arts and Sports. Students also learn about the knowledge and procedures necessary to develop the textbooks necessary for AI convergence education. In particular, the core educational content is to set educational goals and to reorganize and converge subject contents suitable for them. This is a course that covers theories, such as Educational Purpose Theory, Textbook Research Theory, Educational Methodology, and Educational Evaluation Theory. This is a required course for pre- and in-service teachers. This course is intended for the students to analyze teaching materials and textbooks for effective and efficient AI convergence education, and to develop the ability to evaluate textbooks appropriate for AI convergence classes. Therefore, course contents include The Understanding of AI Convergence Education, The Goal of AI Convergence Classes, Learning Contents of AI Convergence Education, Teaching Materials of AI Convergence Education, Teaching-Learning
AAID009 AI Convergence Subject-based Practice 3 6 Major Master 1-5 Korean Yes
This course aims to derive best cases and models of artificial intelligence convergence subject education by exploring examples of various types of AI convergence subject classes currently in progress in the school education field and evaluating them based on specific criteria. In this process, learners need to find out best examples or practice of AI convergence classes by other teachers or discover cases of AI convergence curriculum classes in other countries, find benchmarking points, and apply them to the lessons of individual teachers.
AAID010 Instructional design of AI-based 3 6 Major Master 1-5 Korean Yes
This course aims to explore how advanced intelligent information technology, including artificial intelligence, can be applied to subject education classes. Let's try to understand and practice the entire process of designing, and developing AI-based classes so that students can conduct different classes by applying artificial intelligence in a specific unit class among the subjects in charge of individual teachers as learners.
AAID011 Understanding Big Data Analytics 3 6 Major Master 1-5 English Yes
This course discusses basics of big data analytics. The topics covering in thiscourse are fundamentals of big data, examining big data types. relation of cloudwith big data, operational big data management system, MapReduce fundamentals,Hadoop foundation and ecosystem, big data warehouses, big data analytics.understanding text analytics, integrating data sources, operationalizing big data andsecurity in big data management.
AAID012 Understanding of natural Language Processing 3 6 Major Master 1-5 Korean Yes
Natural Language Processing is one of the important technologies in artificial intelligence convergence. Students can learn natural language processing model based on theory in this course. This course aims to understand the principle by understanding the basic knowledge of the NLP area and further implementing it directly in an online Integrated Development Environment. And this course introduces recent studies related to NLP.
AAID013 AI Education Using Physical Computing 3 6 Major Master 1-5 Korean Yes
In this lecture, we learn about development tools and environments related to open source based microcontroller boards (Arduino). we learn about advanced physical computing techniques closely related with computer engineering and how to apply them to real life. In details, we intensively learn how to connect components through the hardware development of the Arduino project, how to read schematics, how to acquire data sheets, and how to select and use sensors to implement specific functions.
AAID014 Visualization of Bigdata 3 6 Major Master 1-5 Korean Yes
In the era of big data, a lot of data is generated, and many studies are focused on how to derive meaningful information by analyzing the data collected on the basis of big data. Visualization has become an essential factor not only to summarize data, but also to discover hidden meanings in data and to gain insight into new information indicated by the results of data analysis. Big data visualization refers to the visual expression of big data analysis results so that they can easily understand them, and it is the process of turning data into knowledge in a way to effectively understand the results. For big data visualization, learn what big data is, learn data collection methods and various techniques of data analysis, and learn visualization methods suitable for data characteristics. A Python programming language is used to apply the learned materials for big data visualization, and structured data and unstructured data will be covered in the class. The class includes the practice of collecting, analyzing, and visualizing big data, and gives an opportunity to directly or indirectly experience of big data visualization on various topics through a project as a team.
AAID015 AI Convergence Project 1 3 6 Major Master 5 Korean Yes
This course aims the researching and developing the individual project based on the understanding of the artificial intelligence technologies. Lecturer leads the research and development project with the person-to-person lecturing and collaborative discussions. The final output of this course would be the research paper and/or the demoable project implementation.

 

Understanding of Artificial Intelligence and Basis of Data Science

In this lecture, you will understand the core technologies of artificial intelligence and learn cases where related technologies are actually used throughout the industry. Also, this lecture covers the basic concepts of artificial intelligence technology, search&optimization, various methods of knowledge expression, and machine learning methods including deep learning.

Basic Programming for Education of Applied Artificial Intelligence

In this lecture, you will learn the programming basics for convergence education in the AI era. Students who have taken this course aim to develop the ability to perform basic coding tasks using Python programming language. Also, you will write applied artificial intelligence-based educational programs using the programming skills you have learned.

Data Structures and Algorithm for Data Science

This lecture is a basic course for solving problems in data science, which covers the analysis of problems given, the design of data structures, the development of algorithms, and the technology of implementation.

Computational Thinking and Problem Solving

In this lecture, you will learn how to find problems using special patterns and procedures in given circumstances, and how to devise computing programs able to solve these problems. Specifically, this course covers concepts of problem decomposition, pattern matching, abstraction, automation and aims to enable students to apply what they have learned to real situations.

Teaching Methods & Educational Technology using Artificial Intelligence

In this lecture, you will explore various AI-based methods necessary for teaching subjects in school. Also you will conduct mock classes on a specific topics by writing a lesson guide.

AI-based Instructional/learning Materials Development

In this lecture, you will develop learning materials for AI education, and apply it through practices. You will also learn about the hardware and software composition to link with equipment needed for AI education.

Materials and Methods in Applied Artificial Intelligence Education

In this lecture, you will study teaching materials and textbooks for effective and efficient applied artificial intelligence education. Through this course, you will be able to evaluate what are the appropriate materials for AI-based classes.

Understanding and Application of AI based Classes

In this lecture, you will learn relationship between education and AI technology. Also you will be able to understand classes using AI technologies more deeply by looking over the actual cases.

Artificial Intelligence Ethics

This lecture covers the proper attitudes humans should have with the development of artificial intelligence and social issues related with AI development.

AI-based Mathematics·Science Education

In this lecture, you will apply the latest applied AI technology to elementary and secondary education in Mathematics and Science.

AI-based Liberal Arts·Social Studies Education

In this lecture, you will apply the latest applied AI technology to elementary and secondary education in Liberal Arts·Social Studies.

AI-based Arts·Physical Education

In this lecture, you will apply the latest applied AI technology to elementary and secondary education in Arts·Physical Education.

Advanced Programming for Applied Artificial Intelligence Education

In this lecture, you will learn an advanced course of Python, a programming language for artificial intelligence, and Keras, one of the the deep learning framework. You will also implement and test AI's basic Neural Network (CNN, RNN, etc.) model directly.

Data Mining

In this lecture, you will learn various methodologies of data mining, and acquire problem solving skills using those methodologies. At the end of this course, your ability to discover new facts which meet consumer needs by using data mining technologies.

Data Network

This lecture t introduces the basic concepts and elements of design and implementation of the 1) network, 2) protocols, and 3) applications.

Big Data Analytics and Visualization

In this lecture, you will apply statistical data processing, data mining, concepts learned in data analysis, methods and algorithms to practical & large data analysis.

Machine Learning and Deep Learning

In this lecture, you will learn the history of machine learning and deep learning. You will also learn about the the latest AI models such as CNN, RNN, GAN, and LSTN.

Deep Learning Based Computer Vision

This lecture covers the basic contents of computer vision, such as image formation, basic template class, pixel processing, and image classification. Based on these contents, you will learn deep learning-based computer vision technology and develops computer vision-based programs that can be used to solve problems.

AI Education Using Physical Computing

In this lecture, you will learn physical computing, such as Arduino Engineering Basics, Transistor and Drive Motor, and Shift Register. The final goal is to present AI education projects based on what you have learned.

Deep Learning Based Virtual Reality

In this lecture, you will learn about video processing and virtual reality processing technology using the latest deep learning technology.

Natural Language Processing

Through this course, you will learn the core models based on deep learning for natural language processing, and the application areas of these models, such as document classification, machine translation, and question and answer. You will also learn about natural language processing techniques that can be combined with education.

AI Techonology Seminar

This course is a seminar-type course that learns about the latest theories and industrial trends in the field of artificial intelligence, and artificial intelligence techniques that are being applied in the field of education. It provides more realistic and high-quality learning opportunities through inviting outside experts.

Curriculum-related AI Project

In this lecture, you will conduct the entire process of the project from selecting the subject of the AI convergence project, writing proposals, carrying out tasks and reporting based on the acquired major knowledge. It aims to increase the on-site ability through selecting the subject with contents related to various subjects and carrying out projects similar to the company's work.

Industry-Academia related AI Project

In this lecture, you will conduct the entire process of the project from selecting the subject of the AI convergence project, writing proposals, carrying out tasks and reporting based on the acquired major knowledge. By reflecting the demand of industry on the subject, it will help students to experience constructing and solving problems that are actually addressed by the industry.

Research Task

Based on the acquired major knowledge, students select topics related to AI convergence education and draw up a dissertation. The dissertation review committee shall consist of at least three experts in the relevant field, including an academic advisor.