Master of Science in Artificial Intelligence : Woosong University

Master of Science in Artificial Intelligence

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Master of Science in Artificial Intelligence

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Master’s in Artificial Intelligence

Artificial Intelligence (AI) and big data analytics are now central to Industry 4.0, transforming how industries operate and increasing global demand for highly qualified AI specialists. In response to this rapidly evolving landscape, the Endicott College of International Studies (ECIS), Woosong University offers the Master of Science in Artificial Intelligence, a graduate program designed to equip students with both strong theoretical foundations and hands-on applied competence in AI technologies.

Overview

IntakeSpring & Fall
Duration2 years
Credits27
TypeFull-time
FormatOffline
LanguageEnglish

What makes our program unique?

The program curriculum is structured to ensure broad competence as well as deep specialization. Students complete a set of universityrequired courses offered every semester, including Ethical AI / DecisionMaking, Principles of AI, and Generative AI for Business, providing essential preparation in responsible AI use, core AI concepts, and realworld business applications.

Structure

The program curriculum is structured to ensure broad competence as well as deep specialization. Students complete a set of university-required courses offered every semester, including Ethical AI / Decision-Making, Principles of AI, and Generative AI for Business, providing essential preparation in responsible AI use, core AI concepts, and realworld business applications. In addition, the program offers graduate-level department-required courses organized across four semesters. The course pathway covers key areas of contemporary AI, including:

Computer Vision, Data Analysis, Neural Network Architectures, and Cloud AI Solutions (1st semester)
Deep Learning, Advanced Statistics, Multi-Agent Systems, and Natural Language Processing (2nd semester)
Capstone Project, Robotics, AI-Based Data Mining, and Reinforcement Learning (3rd semester)
Advanced topics including Large Language Models (LLM), AI System Design, Thesis Research, Social Media Analytics, and AI and Law (4th semester)

Courses are offered on a semester basis. In each semester, only a limited number of courses are opened depending on demand and other practical considerations. As a result, students are not required to register for all courses at the same time. This program prepares graduates for advanced AI careers and further academic research by combining ethical awareness, technical depth, and practical project-based learning.

Credit Classification	Thesis	Non-thesis
Core	9	9
Major Required	6	6
Major Elective	3	6
General Elective*	6	6
Thesis

* General Elective credits are any additional credits that can be earned in any category (Core, Major Required, or Major Elective)

Students are required to achieve a cumulative GPA of 3.0 or higher to graduate.

Curriculum

Credit type	Semester	Name of the Course	Credits
University Required Courses	Every Semester	Ethical AI / Decision-Making ▼ Course Description This course examines the ethical, social, and governance implications of AI in business and society. Students will learn frameworks for responsible AI use, addressing bias, fairness, and compliance issues. The course also explores how generative AI can enhance strategic decision-making by evaluating risks, optimizing choices, and improving business outcomes.	3
		Principles of AI ▼ Course Description This course introduces the fundamental concepts, history, and key technologies of artificial intelligence. Students will learn about various applications of AI, including machine learning, natural language processing, and computer vision, while understanding the design and implementation processes of AI systems. The course provides hands-on experience in implementing simple AI models.	3
		Generative AI for Business ▼ Course Description This course is designed to help students from various majors easily understand the concepts and business applications of Generative Artificial Intelligence. It introduces the basic principles and key technologies of generative AI, exploring practical use cases such as text and image generation for business innovation. Students will also gain hands-on experience applying generative AI in marketing, customer analysis, and content creation, developing foundational problem-solving skills using AI tools.	3
Department Required Courses	1st semester	Computer Vision ▼ Course Description This course covers the fundamental principles and applications of computer vision. Students will learn about image processing, object recognition, image segmentation, and deep learning-based visual recognition technologies. Through practical projects, they will analyze real image data and implement various computer vision algorithms while exploring the latest research trends.	3
		Data Analysis ▼ Course Description This course teaches the foundational principles and techniques of data analysis. Students will learn statistical methods, data visualization, data exploration, and preprocessing techniques, conducting projects that analyze real data using various data analysis tools (e.g., Python, R). This course emphasizes the importance of data-driven decision-making.	3
		Neural Network Architectures ▼ Course Description This course focuses on various neural network architectures. Starting from basic perceptrons, students will explore convolutional neural networks (CNNs), recurrent neural networks (RNNs), and recent transformer models. They will understand the features and advantages of each architecture and learn how to apply them to real data for performance evaluation.	3
		Cloud AI Solutions ▼ Course Description This course focuses on the development and deployment of AI solutions in cloud environments. Students will learn about cloud service models and data processing technologies, engaging in projects to implement cloud-based AI applications.	3
	2nd semester	Deep Learning ▼ Course Description This course covers the fundamental concepts and various architectures of deep learning. Students will understand the structure and learning methods of deep neural networks, gaining experience in building and optimizing deep learning models through practical exercises.	3
		Advanced Statistics ▼ Course Description This course covers advanced concepts and techniques in statistics. Students will learn various statistical methods, including regression analysis, multivariate analysis, and Bayesian statistics, fostering the statistical reasoning needed for data interpretation. Practical exercises will provide experience in applying statistical techniques to real data.	3
		Multi-Agent Systems ▼ Course Description This course covers the concepts and applications of multi-agent systems. Students will learn about interactions and cooperation models among agents, applying their knowledge in projects that solve real-world problems.	3
		Natural Language Processing ▼ Course Description This course covers the basic concepts and techniques of natural language processing (NLP). Students will learn about text preprocessing, language modeling, sentiment analysis, and machine translation, gaining skills in building and evaluating NLP models. They will gain practical experience through projects that utilize real text data.	3
	3rd semester	Capstone Project ▼ Course Description The Capstone Project is a culminating experience designed for students in the graduate program, allowing them to apply the knowledge and skills acquired throughout their studies to a real-world problem or research question. In this course, students will identify a specific issue related to artificial intelligence or data science, formulate a project proposal, and develop a comprehensive solution. They learn Problem Identification, Project Planning, Implementation, Collaboration, Presentation and Documentation.	3
		Robotics ▼ Course Description This course addresses the integration of AI and robotics technology. Students will learn about sensors, control, and AI techniques in robotics, undertaking projects to design and implement robotic systems.	3
		AI-Based Data Mining ▼ Course Description This course covers data mining techniques using AI technologies. Students will learn how to extract useful information from large datasets and undertake projects that analyze real datasets to derive insights.	3
		Reinforcement Learning ▼ Course Description This course provides an in-depth look at the theories and applications of reinforcement learning. Students will learn how agents interact with environments to learn optimal behaviors and will familiarize themselves with concepts such as Markov Decision Processes (MDP), policy gradient methods, and deep reinforcement learning. They will apply these theories in projects that solve real reinforcement learning problems.	3
	4th semester	Large Language Model ▼ Course Description The Large Language Models (LLM) course explores the foundational concepts, architectures, and applications of large-scale language models in natural language processing (NLP). This course is designed for students interested in understanding the mechanics of LLMs and their impact on various domains.	3
		AI System Design ▼ Course Description This course focuses on the design and implementation of AI systems. Students will learn about system architecture, performance evaluation, requirements analysis, and development processes. Through theoretical foundations and real-world case studies, they will explore the design of AI solutions and their applications across various industries. Team projects will provide opportunities for collaboration and problem-solving skills development.	3
		Thesis Research ▼ Course Description This course is an area of study or research necessitating a high level of self-directed learning. This learning requires students to read, conduct research, complete written examinations, reports, projects, research papers, portfolios, or similar assignments that are designed to measure competency in the stated educational objectives. The work will be related to an academic discipline done outside of the formal (directly supervised) classroom. This research may be experiential, directed reading, or independent research supervised by a faculty advisor and approved by the chairperson of the department under which the course is listed.	3
		Social Media Analytics ▼ Course Description This course teaches techniques for analyzing social media data. Students will understand the impact of social media through sentiment analysis, trend detection, and user behavior analysis.	3
		AI and Law ▼ Course Description This course explores the legal and ethical issues surrounding AI technologies. Students will discuss issues such as algorithmic transparency, data privacy, and legal accountability, gaining an understanding of the impacts of AI technologies on the legal environment.	3

*Courses are offered on a semester basis. In each semester, only a limited number of courses are opened, depending on demand and other practical considerations. As a result, students are not required to register for all courses at the same time.

Course Inquiries: Dr. Hasan Tinmaz, Program Coordinator of the AI Master’s and PhD Programs (htinmaz@endicott.ac.kr)

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