For more details on the courses, please refer to the Course Catalog
| Code | Course Title | Credit | Learning Time | Division | Degree | Grade | Note | Language | Availability |
|---|---|---|---|---|---|---|---|---|---|
| ADS5002 | Basic Statistics | 3 | 6 | Major | Master/Doctor | 1-8 | Applied Data Science | Korean | Yes |
| In this course you will learn how to program in R and how to use R for effective data analysis. You will learn how to install and configure software necessary for a statistical programming environment, discuss generic programming language concepts as they are implemented in a high-level statistical language. The course covers practical issues in statistical computing which includes programming in R, reading data into R, accessing R packages, writing R functions, debugging, and organizing and commenting R code. | |||||||||
| ADS5006 | Advanced Machine Learning | 3 | 6 | Major | Master/Doctor | 1-8 | Applied Data Science | Korean | Yes |
| This course provides a broad introduction to machine learning, datamining, and statistical pattern recognition. Topics include: (i) Supervised learning (parametric/non-parametric algorithms, support vector machines, kernels, neural networks). (ii) Unsupervised learning (clustering, dimensionality reduction, recommender systems, deep learning). (iii) Best practices in machine learning (bias/variance theory; innovation process in machine learning and AI). The course will also draw from numerous case studies and applications, so that you'll also learn how to apply learning algorithms to building smart robots (perception, control), text understanding (web search, anti-spam), computer vision, medical informatics, audio, database mining, and other areas. | |||||||||
| 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. | |||||||||
| ADS5034 | Computer Vision | 3 | 6 | Major | Master/Doctor | 1-8 | Applied Data Science | Korean | Yes |
| This course focuses in the study of theories for image analysis. The first part consists of Image formulation model, early processing, boundary detection, region growing and segmentation, motion detection, merging and introduction of morphology. The second part, we cover basic concepts of statistical model, dis- criminant function, decision boundary and rules and neural network for visual pattern recognition. | |||||||||
| 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. | |||||||||
| 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 |
| 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. | |||||||||
| AIM5053 | AI and Ethics | 3 | 6 | Major | Master/Doctor | Artificial Intelligence | - | No | |
| In this course, students will analyze AI models' limitations in terms of ethics and learn how to overcome the limitations. Every technology has an intended use and unintended consequences. For example, nuclear power makes power plants and atomic bombs. AI also has this dual-use. Students will learn the problem of dual-use in AI and understand and suggest solutions. | |||||||||