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 | - | No |
| 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. | |||||||||
| 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 | - | No | |
| 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 | - | No |
| 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 |
| ThislessondiscussesbasictechnologiesonInput,processinganddisplayingofvisualsignals.Mainsubjectsareimagealgebra,imageenhancementtechniques,edgedetection,thresholding,thinningandskeletonizing,morphologicaltransforms,linearimagetransforms,patternmatchingandshapedetection,imagefeaturesanddescriptors,deepneuralnetworks,andsoon. | |||||||||
| AIM5021 | Natural Language Processing Theory and applications | 3 | 6 | Major | Master/Doctor | 1-4 | Artificial Intelligence | Korean | Yes |
| Naturallanguageprocessing(NLP)isoneofthemostimportanttechnologiesoftheinformationage.Understandingcomplexlanguageutterancesisalsoacrucialpartofartificialintelligence.TherearealargevarietyofunderlyingtasksandmachinelearningmodelsbehindNLPapplications.Inthiscoursestudentswilllearntoimplement,train,debug,visualizeandinventtheirownneuralnetworkmodels.Thecourseprovidesathoroughintroductiontocutting-edgeresearchindeeplearningappliedtoNLP.thiscoursewillcoverwordvectorrepresentations,window-basedneuralnetworks,recurrentneuralnetworks,long-short-term-memorymodels,recursiveneuralnetworks,convolutionalneuralnetworksaswellassomerecentmodelsinvolvingamemorycomponent. | |||||||||
| 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 |
| Inordertouserobotsveryefficiently,robotsarerequestedtobeabletoperformalltasksashumanscan.Thiscoursediscussesthetechniqueofsensoranditsapplicationinordertomakerobotsperformtasksintelligently. | |||||||||
| AIM5026 | Introduction to Robotic Intelligence | 3 | 6 | Major | Master/Doctor | Artificial Intelligence | - | No | |
| Robot is defined as an intelligent system connecting sensors and actuators. As an intelligent system, robot is to play a key role for providing necessary services to human by automatically carrying out tasks requiring navigation and manipulation. To this end, robot needs to recognize objects and understand surroundings while reasoning and planning the behaviors necessary for carrying out tasks. Especially, it is essential for robot to be able to obtain its capabilities of recognition and understanding of environments as well as of reasoning and planning of behaviors by learning. This course deals with the fundamentals of robot intelligence on how robot learns for the recognition and understanding of environments as well as for the reasoning and planning of behaviors associated with manipulation and navigation. | |||||||||