Graduate Program in Artificial Intelligence
Educational Goals
As a core technology of the 4th Industrial Revolution, the success of Artificial Intelligence hinges on securing actionable data and effectively integrating it to generate intelligent insights.
The Graduate School of AI is dedicated to cultivating outstanding, interdisciplinary talent prepared to lead in specialized AI+X domains, including: Maritime, Port, Logistics & Manufacturing, Robotics & Medical, and Healthcare Industry & Smart City Industry. Our curriculum is centered on the practical application and convergence of AI within these key industries
Additionally, we aim to develop experts in intelligent IoT services, edge computing, and cloud-based technologies, building upon the essential ICT infrastructure required for modern AI services. By developing machine learning, algorithms, and integrated services for artificial intelligence technology, fostering AI practical experts, promoting industry-academia collaboration & practical problem-solving, industry-academia on-site collaboration research, and continuing education programs for professionals, our goal is to elevate local industries to a global standard of technological excellence and secure a new industrial growth engine for Busan.
Major Fields
| Major Fields | Overview |
|---|---|
| Artificial Intelligence | This track focuses on the core algorithms and theoretical foundations of AI. Students delve into both fundamental and advanced concepts to build the expertise needed to develop innovative AI solutions. |
| AI Systems | This track concentrates on the ICT infrastructure essential for deploying AI services and aims to foster talents focused on hardware-software integration and systems. |
Curriculum
1Core Courses
| Course Name | Credits |
|---|---|
| Introduction to Artificial Intelligence | 3 |
|
Students will learn a wide range of fundamental AI algorithms, explore the latest research trends, and understand their application across various domains The curriculum also examines real-world case studies where AI technologies are being effectively applied |
|
| Introduction to Big Data | 3 |
|
This course introduces the core concepts and technological trends of big data. Students will gain hands-on experience with the entire data lifecycle—from collection and cleaning to processing, analysis, and application—using modern tools like Python Building on this foundation, students will learn to identify big data challenges in their own fields and apply core technologies to solve them |
|
| Introduction to Machine Learning | 3 |
| This course covers the foundational concepts of machine learning. Topics include supervised learning models (regression, classification), unsupervised learning models (clustering, dimensionality reduction), ensemble methods, and Bayesian machine learning methodologies. | |
| Programming for Artificial Intelligence | 3 |
| This course focuses on the programming methodologies essential for implementing AI algorithms and building deep learning models. Students will gain practical, hands-on experience in developing sophisticated AI-based programs | |
| Introduction to Data Mining | 3 |
| This course explores the analysis of various data types through a wide range of data mining algorithms. Students will work with techniques such as clustering, association analysis, case-based reasoning, association rule discovery, artificial neural networks, and decision trees | |
| Introduction to Intelligent Robotics | 3 |
| This course provides a foundational understanding of intelligent robotics. It begins with the core components—sensors, controllers, and actuators—and then explores advanced systems and industry trends, including the Robot Operating System (ROS), autonomous navigation, stereo vision systems, and 3D-LiDAR applications | |
| Introduction to Internet of Things | 3 |
|
This course offers a comprehensive overview of IoT theory and applications The curriculum places special emphasis on IoT application services, system architecture, networking technologies, and communication protocols |
|
| Mathematics for Artificial Intelligence | 3 |
|
This course delivers the essential mathematical knowledge required to understand and develop AI algorithms It covers key concepts and theorems in multivariate analysis, linear algebra, probability and statistics, calculus, and discrete mathematics, all grounded in practical application case studies |
|
2Major Courses
| Course Name | Credits |
|---|---|
| Advanced Data Mining | 3 |
| This advanced course covers key methodologies and application systems for both structured and unstructured data. Students will delve into classification, regression, clustering, and association rules through real-world case studies | |
| Advanced Deep Learning | 3 |
| This course provides an in-depth understanding of artificial neural networks, the core of modern deep learning. Students will master a wide range of advanced algorithms derived from these foundational models | |
| Reinforcement Learning | 3 |
|
This course explores reinforcement learning, a machine learning paradigm inspired by behavioral psychology for intelligent decision-making Students will learn the mathematical foundations of RL algorithms, analyze properties such as convergence and optimization, and apply these techniques to solve practical problems |
|
| Recommendation Systems | 3 |
| This course delves into recommendation systems, one of the earliest and most widespread applications of AI. Students will analyze core recommendation algorithms and gain practical experience by building systems with real-world data | |
| Computer Vision System | 3 |
| This course focuses on the design and implementation of application systems that utilize computer vision. Students will learn about various image recognition algorithms and how to apply them effectively based on specific image characteristics and project goals | |
| Natural Language Processing | 3 |
| This course covers the key deep learning models essential for NLP. Through hands-on programming and analysis, students will apply fundamental NLP concepts to develop their own language-based services | |
| Digital Image Processing | 3 |
| This course explores the fundamental methods of image processing required for systems handling digital images. Topics include the entire operational pipeline: data acquisition, transformation, enhancement, and storage | |
| Data Visualization | 3 |
|
This course provides a strong foundation in the principles of data visualization Students will learn visualization techniques, interaction methodologies, and visual perception theories, applying them in conjunction with statistics and machine learning to build effective data visualization systems |
|
| AI Business Platform | 3 |
| This course focuses on the intersection of AI and business. Students will learn to build AI-driven business models, analyze successful application cases, and develop effective market strategies | |
| Chatbot System | 3 |
| This course covers the theoretical background and essential technologies required to build sophisticated chatbots. It focuses on system architecture for Korean-language chatbots The course also includes a comparative analysis of leading open-source engines for practical implementation | |
| AI Service Design | 3 |
| This course explores service design in the age of AI. Students will use established service design methodologies and modern AI technologies to analyze service processes and develop innovative business models | |
| Intelligent System Project I | 3 |
| This course allows students to experience the development process by integrating artificial intelligence tailored to industry demands | |
| Intelligent System Project II | 3 |
| This course allows students to experience the development process by integrating artificial intelligence tailored to industry demands | |
| Innovation and Entrepreneurship | 3 |
| This course equips students with an entrepreneurial mindset to navigate an era of rapid technological change. Students will learn to identify high-potential opportunities and develop a robust entrepreneurial vision | |
| Deep Learning System | 3 |
| This course delves into the practical technologies required for implementing deep learning algorithms and building real-world, scalable systems. | |
| Advanced Machine Learning | 3 |
| This course covers both traditional and cutting-edge machine learning techniques, cultivating a student's ability to critically analyze and apply a wide range of complex algorithms. | |
| AI Time Series Data Analysis | 3 |
| This course introduces advanced theories and algorithms for representing and analyzing time series data, focusing on methods for extracting insights from diverse real-world datasets. | |
| Advanced Smart Port | 3 |
| Port logistics data exists in various forms like video and time series. This course consists of classes on the algorithms of port-specific logistics scheduling, port safety systems using video analytics, and deep neural network analysis for maritime data, provides a comprehensive understanding of AI applications in smart ports. | |
| Mobile Robotics | 3 |
| This course covers the fundamental theories of mobile robotics, which are increasingly vital in modern manufacturing. Students will use the Robot Operating System (ROS) to learn AI-based environmental perception (using sensors like LiDAR and cameras) and master techniques for autonomous path planning and localization | |
| Smart City and Big Data Analysis | 3 |
| This course explores the smart city as an integrated platform of IoT, Big Data, and AI. Utilizing these technologies, it facilitates data collection, security and legal frameworks, and data-driven urban planning to develop various applications in mobility, energy, and the environment. Students will learn about the foundational technologies for building smart cities, operating them through data analysis, and creating AI-driven services, complete with advanced case studies. | |
| Advanced Smart Healthcare | 3 |
| Healthcare is a dynamic field for AI research, applicable in various areas such as diagnosis, prediction, and personalized health management. Students will learn healthcare-specific techniques, including advanced image processing algorithms for diagnosis, signal processing for time series analysis, and NLP for personalized health management. | |
| Advanced Industrial Artificial Intelligence | 3 |
| In real-world industries like manufacturing and logistics, there are various problems that can be solved using AI techniques, such as optimization and prediction. Students will learn to define and tackle challenges from industries like manufacturing and logistics, applying sophisticated optimization and prediction techniques using AI. | |
| Explainable Artificial Intelligence (XAI) | 3 |
| This course focuses on XAI, a critical set of methodologies for understanding and interpreting complex machine learning models Students will learn the operating principles of various XAI techniques to build more reliable, transparent, and trustworthy AI systems for critical decision-making. | |
| AI Interpretation and Interaction | 3 |
|
The interpretability of AI technology and user interfaces for human-centered technology utilization are becoming increasingly important This course covers various model interpretation techniques and the design of interactive user interfaces applicable to modern deep learning methods |
|
| AI Optimization Theory | 3 |
| This course focuses on techniques to enhance the performance of AI algorithms. Topics include distributed computing, model compression and quantization (lightweighting), and full-stack system optimization | |
| Big Data Processing Platform | 3 |
|
This course provides an in-depth study of the Apache Hadoop Ecosystem, a leading platform for big data processing Students will master core projects like HDFS and MapReduce and explore related technologies such as Hbase, Kafka, and Spark |
|
| AI Security | 3 |
| With the application of AI, the importance of security has grown across various sectors, including manufacturing, finance, and healthcare. Students will learn about security principles from the network, database, and system perspectives | |
| Intelligent Robot Design and Applications | 3 |
| This course covers learning about the ROS (Robot Operating System)-based autonomous driving robot platform. It covers the practical application of sensors like stereo cameras and LiDAR to drive and control autonomous robots for various tasks. | |
| AI System Analysis and Architecture | 3 |
| This course covers the essentials of real-world AI system development, including requirements analysis, hardware configuration, software architecture, and design patterns. | |
| Cloud Computing | 3 |
|
This course covers both the fundamental theory of cloud computing and the practical skills needed to develop on cloud platforms Specific topics include cloud service models, virtualization technologies, and cloud-native programming techniques |
|
| Advanced Blockchain | 3 |
|
Blockchain is a distributed data storage environment based on P2P methods, where managed data, referred to as 'blocks', are linked in a chain. This ledger management technology, based on distributed computing, ensures that no one can arbitrarily modify stored data, and anyone can view the results of changes Students will learn its core technologies and work on practical implementation projects |
|
| Innovative R&D Project I | 3 |
| This course involves participating in industry-academia projects within the field of AI autonomous research | |
| Innovative R&D Project II | 3 |
| This course involves participating in industry-academia projects within the field of AI autonomous research | |
| Directed Research (Master's) 2 | 2 |
| Directed Research (Doctoral) I 2 | 2 |
| Directed Research (Doctoral) II | 2 |