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. | |||||||||
| AIM4001 | Advanced Big Data Analytics | 3 | 6 | Major | Bachelor/Master | - | 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 | - | 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 | 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 | - | 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 | Korean | Yes | |
| MachineLearningisthestudyofhowtobuildcomputersystemsthatlearnfromexperience.Thiscoursewillgiveanoverviewofmanymodelsandalgorithmsusedinmodernmachinelearning,includinggeneralizedlinearmodels,multi-layerneuralnetworks,supportvectormachines,Bayesianbeliefnetworks,clustering,anddimension reduction. | |||||||||
| AIM5003 | Theory of Pattern Recognition | 3 | 6 | Major | Master/Doctor | - | No | ||
| Thiscourse covers the basictechnologies on processing and recognition of digital image patterns. Main subjects are statistical patternrecognition, supervised learning, linear discrimination functions, unsupervised learning, syntactic patternrecognition, parsing and grammars, graphical syntactic patternrecognition, grammatical inference, neural patternrecognition, and so on | |||||||||
| AIM5004 | Deep Neural Networks | 3 | 6 | Major | Master/Doctor | - | 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. | |||||||||
| AIM5007 | Bigdata Processing Platform | 3 | 6 | Major | Master/Doctor | 1-4 | - | No | |
| This ourse covers Had oopand Hadoop Eco System which is a group of applications based on and working with Hadoop. Students learn Hadoop architecture, software stack and principle of its processesl ikemap- reduce. Students study Hadoop eco system,l ike Hive, hbase, Spark, scoop, flume, kafka, Azkaban, ambari,etc. | |||||||||
| AIM5008 | Special Topics in Artificial Intelligence and Simulation | 3 | 6 | Major | Master/Doctor | 1-4 | - | No | |
| This course deals with various topics on the integration of Artificial Intelligence techniques and simulations. AI is used in the model structure representation, model construction and management of simulation model using knowledge representation techniques. Expert systems is used to represent models which have complex state transition. This course covers various topics related to those. | |||||||||
| AIM5009 | Evolutionary Algorithm | 3 | 6 | Major | Master/Doctor | 1-4 | - | No | |
| Evolutionary Algorithms s one of soft computing techniques for intelligent searchin gproblems, the genetic methodologies such as genetical gorithms,evolutionary programming,etc., are introducedwith anemphasis inengi eering perspectives. The detailed description of computer implementation of genetic algorithm is also given including selection of genepopulation, chromosome, crossover, mutation, etc. With the introduced back ground material, one can solve the search problems for optimal solution given as intelligent control examples. | |||||||||
| AIM5010 | Advanced Reinforcement Learning | 3 | 6 | Major | Master/Doctor | 1-4 | - | 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. | |||||||||
| AIM5011 | Numerical Analysis for AI | 3 | 6 | Major | Master/Doctor | 1-4 | - | No | |
| The oal of this course is to enable students with little or no programming background to solve common computational problems in artificial intelligence. Matlab and/or Python programming will be covered, together with basic principles of computer architecture and arithmetic. Basic numerical techniques in numerical differentiation, integration, linearalgebra, differential equations, and statistics, are covered and applied to mathematical analysis in artificial intelligence field. Emphas is will be placed on enabling students to use currently available numerical methods to solve engineerin gproblems. | |||||||||
| AIM5013 | Theory of Probability and Random Process | 3 | 6 | Major | Master/Doctor | 1-4 | - | No | |
| The im of this course is to develop a thorough understanding of the principles of random processes and knowledge of applying them to some important problems. First, the basic theory in probability and randomprocess is introduced, paying particular attention to the multi variate Gaussian density function. Then, the theory of randomprocesses and their characterization by autocorrelation and power spectral density functions is developed. The theory is then applied to the design of optimum linear systems. | |||||||||
| AIM5014 | Theory of Digital Integrated Circuit Design | 3 | 6 | Major | Master/Doctor | 1-4 | - | No | |
| This coursecoversstructuresandoperationalprinciplesofCMOStransistorsanddigitalcitcuits(INV,NAND,NOR,LATCH,CurrentMirror),computationofsizinganddelays,FlashA/Dconverter. | |||||||||