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Graduate -


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

Code Course Title Credit Learning Time Division Degree Grade Note Language Availability
ADS5021 Advanced in Information Security 3 6 Major Master/Doctor 1-8 Applied Data Science - No
This course focuses on the fundamentals of information security that are used in protecting both the information present in computer storage as well as information traveling over computer networks. Interest in information security has been spurred by the pervasive use of computer-based applications such as information systems, databases, and the Internet. In this course, we will consider such topics as fundamentals of information security, computer security technology and principles, access control mechanisms, cryptography algorithms, software security, physical security, and security management and risk assessment.
ADS5032 Data Science Applications 3 6 Major Master/Doctor 1-8 Applied Data Science - No
Learning how to apply Data Science in real applications is important. After understanding the fundamentals of Data Science, students will be introduced to various methods in applying data science in different domains or practical applications. Latest topics in Data Science will be introduced.
ADS5035 Data-drivenSecurityandPrivacy 3 6 Major Master/Doctor 1-8 Applied Data Science Korean Yes
This course is about the role of data and data analytics in security and privacy. This course focuses on applications of machine learning and big data analytics to various security and privacy problems, using various AI techniques to solve challenging security and privacy issues.
AIM4001 Advanced Big Data Analytics 3 6 Major Bachelor/Master Artificial Intelligence - No
This course introduces fundamental data mining and machine learning techniques for big data analytics. The emphasis in the course will be learning key techniques that are required to extract meaningful information from big data, and developing scalable data mining algorithms for big data analytics. The first half of the course will cover various supervised and unsupervised machine learning methods (theoretical analysis of the methods and their practical applications), and the last half of the course will focus on scalable graph mining techniques with special emphasis on analyzing large-scale social networks. There will be one midterm, three assignments, and the final project where students will be expected to develop scalable algorithms for collecting and analyzing big data.
AIM4002 Biomedical Artificial Intelligence 3 6 Major Bachelor/Master 1-4 Artificial Intelligence - No
Biomedical research is one of the most exciting application domains of artificial intelligence, with transformative potential in areas of precision medicine. The goal of this course is to introduce the underlying concepts, methods, and the potential of intelligent systems in biomedicine. The course aims to provide students from diverse backgrounds with both conceptual understanding and practical grounding of cutting-edge research on AI in biomedicine in the areas of deep learning, bioinformatics, computational models, and data science. As a research and project-based course, student(s) will have opportunities to identify and specialize in particular AI methods, biomedical applications, and relevant tools. The course is designed to be accessible to non-quantitative majors but will require prior programming experience.
AIM4003 Natural Language Processing Fundamentals 3 6 Major Bachelor/Master 1-4 Artificial Intelligence Korean Yes
his course covers the overall content of theories and techniques for analyzing and generating natural languages. This course deals with NLP overview, text corpus lexical resources, preprocessing, POS tagging, text vectorization, document classification, syntax analysis, semantic analysis, word embeddings, summarization, deep learning based language models. After taking this course, students are expected to implement programs to solve text problems. To take this course, students are required to have sufficient knowledge in machine learning, deep learning, and Python programming.
AIM5001 Theories of Artificial Intelligence 3 6 Major Master/Doctor Artificial Intelligence Korean Yes
In this course students will learn the fundamental algorithms of Aritificial Intelligence including the problem solving techniques, search algorithms, logical agents, knowledge representation, inference, and planning. After taking the course, students are expected to implement the algorithms using computer programming languages.
AIM5002 Theory of Machine Learning 3 6 Major Master/Doctor 1-4 Artificial Intelligence Korean Yes
MachineLearningisthestudyofhowtobuildcomputersystemsthatlearnfromexperience.Thiscoursewillgiveanoverviewofmanymodelsandalgorithmsusedinmodernmachinelearning,includinggeneralizedlinearmodels,multi-layerneuralnetworks,supportvectormachines,Bayesianbeliefnetworks,clustering,anddimension reduction.
AIM5004 Deep Neural Networks 3 6 Major Master/Doctor Artificial Intelligence - No
In this class, we will cover the following state-of-the-art deep learning techniques such as linear classification, feedforward deep neural networks (DNNs), various regularization and optimization for DNNs, convolutional neural networks (CNNs), recurrent neural networks (RNN), attention mechanism, generative deep models (VAE, GAN), visualization and explanation.
AIM5010 Advanced Reinforcement Learning 3 6 Major Master/Doctor 1-4 Artificial Intelligence Korean Yes
Reinforcement learning is one powerful paradigm for an enormous range of tasks, including robotics, game playing, consumer modeling and healthcare. In this class, we will provide a solid introduction to the field of reinforcement learning including Markov decision process, planning by dynamic programming, model-free prediction, model-free control, value function approximation, policy gradient methods, integrating learning and planning, exploration and exploitation.
AIM5020 Theory of Computer Vision 3 6 Major Master/Doctor 1-4 Artificial Intelligence Korean Yes
AIM5021 Natural Language Processing Theory and applications 3 6 Major Master/Doctor 1-4 Artificial Intelligence Korean Yes
AIM5022 Information Retrieval Theory 3 6 Major Master/Doctor 1-4 Artificial Intelligence - No
Information Retrieval (IR) includes the theory and practical techniques for search engines. In this course, we will cover the models and methods for representing, indexing, searching, browsing, and summarizing information in response to a person's information need. In addition, we will deal with recent advances in neural information retrieval models.
AIM5024 Recommendation Systems 3 6 Major Master/Doctor 1-4 Artificial Intelligence - No
A recommendation system is the information filtering system that seeks to predict the rating or preference that a user would give to a target item. In this course, we will cover non-personalized recommender systems, content-based and collaborative techniques. We also cover nearest neighborhood methods and matrix factorization methods. Lastly, we will address the recent advances in recommender systems using deep neural networks.
AIM5025 Intelligent Robot and System 3 6 Major Master/Doctor 1-4 Artificial Intelligence - No