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  • Department of Digital Media Communication Engineering
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Department of Digital Media Communication Engineering

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

교육과정
Code Course Title Credit Learning Time Division Degree Grade Note Language Availability
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.
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,Korean Yes
MachineLearningisthestudyofhowtobuildcomputersystemsthatlearnfromexperience.Thiscoursewillgiveanoverviewofmanymodelsandalgorithmsusedinmodernmachinelearning,includinggeneralizedlinearmodels,multi-layerneuralnetworks,supportvectormachines,Bayesianbeliefnetworks,clustering,anddimension reduction.
AIM5003 Theory of Pattern Recognition 3 6 Major Master/Doctor Artificial Intelligence - No
Thiscourse covers the basictechnologiesonprocessingandrecognitionofdigitalimagepatterns.Mainsubjectsarestatisticalpatternrecognition,supervisedlearning,lineardiscriminationfunctions,unsupervisedlearning,syntacticpatternrecognition,parsingandgrammars,graphicalsyntacticpatternrecognition,grammaticalinference,neuralpatternrecognition,andsoon
AIM5004 Deep Neural Networks 3 6 Major Master/Doctor Artificial Intelligence Korean Yes
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.
AIM5005 Database Theory of applications 3 6 Major Master/Doctor 1-4 Artificial Intelligence - No
Inthiscourse,weintroducedatabasedesignmethodologyfundamentalforconstructinginformationsystemsandinvestigatepracticaldatabasedesigncasestudy.Themajorcontentsthatwillbecoveredisthefollowing:datamodelingconcepts,conceptualdesignmethodologies,viewdesignandintegration,improvingqualityofdatabaseschema,highleveldesignusingERmode,logicaldesignforrelationalmode,reverseengineering,designtheoryforrelationaldatabases,physicaldatabasedesign,etc.
AIM5006 Theory of Image Processing 3 6 Major Master/Doctor 1-4 Artificial Intelligence - No
Thisclassprovidesfundamentalknowledgeforacquisition,processing,displayofdigitalimagesignalsbystudyingsuchtopicsasmathematicalmodelingofimagesignal,sampling,spatialandtemporalresolution,humanvisualsystem,quantizationtheory,basic2Dsignalprocessing,2Dtransform,frequencyanalysis,filtering,imageenhancement,colorspace,colorprocessing,andcompressionandreconstruction.Selectedpracticalapplicationsareanalysedforbetterunderstandingofsuchtechniques.
AIM5007 Bigdata Processing Platform 3 6 Major Master/Doctor 1-4 Artificial Intelligence - No
ThiscoursecoversHadoopandHadoopEcoSystemwhichisagroupofapplicationsbasedonandworkingwithHadoop.StudentslearnHadooparchitecture,softwarestackandprincipleofitsprocesseslikemap-reduce.StudentsstudyHadoopecosystem,likeHive,hbase,Spark,scoop,flume,kafka,Azkaban,ambari,etc.
AIM5008 Special Topics in Artificial Intelligence and Simulation 3 6 Major Master/Doctor 1-4 Artificial Intelligence - 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 Artificial Intelligence - No
Evolutionary Algorithms asoneofsoftcomputingtechniquesforintelligentsearchingproblems,thegeneticmethodologiessuchasgeneticalgorithms,evolutionaryprogramming,etc.,areintroducedwithanemphasisinengineeringperspectives.Thedetaileddescriptionofcomputerimplementationofgeneticalgorithmisalsogivenincludingselectionofgenepopulation,chromosome,crossover,mutation,etc.Withtheintroducedbackgroundmaterial,onecansolvethesearchproblemsforoptimalsolutiongivenasintelligentcontrolexamples.
AIM5010 Advanced Reinforcement Learning 3 6 Major Master/Doctor 1-4 Artificial Intelligence Korean,English 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.
AIM5011 Numerical Analysis for AI 3 6 Major Master/Doctor 1-4 Artificial Intelligence - No
Thegoalofthiscourseistoenablestudentswithlittleornoprogrammingbackgroundtosolvecommoncomputationalproblemsinartificial intelligence.Matlaband/orPythonprogrammingwillbecovered,togetherwithbasicprinciplesofcomputerarchitectureandarithmetic.Basicnumericaltechniquesinnumericaldifferentiation,integration,linearalgebra,differentialequations,andstatistics,arecoveredandappliedtomathematical analysis in artificial intelligence field.Emphasiswillbeplacedonenablingstudentstousecurrentlyavailablenumericalmethodstosolveengineeringproblems.
AIM5012 Optimization Theory and applications 3 6 Major Master/Doctor 1-4 Artificial Intelligence - No
Linearprogramming,nonlinearprogramming,iterativemethodsanddynamicprogrammingarepresented,especiallyastheyrelatetooptimalcontrolproblems.DiscreteandcontinuousoptimalregulatorsarederivedfromdynamicprogrammingapproachwhichalsoleadstotheHamilton-Jacobi-BellmanEquationandtheMinimumPrinciple.Minimumenergyproblems,lineartrackingproblems,outputregulatorsandminimumtimeproblemsareconsidered.
AIM5013 Theory of Probability and Random Process 3 6 Major Master/Doctor 1-4 Artificial Intelligence - No
Theaimofthiscourseistodevelopathoroughunderstandingoftheprinciplesofrandomprocessesandknowledgeofapplyingthemtosomeimportant problems.First,thebasictheoryinprobabilityandrandomprocessisintroduced,payingparticularattentiontothemultivariateGaussiandensityfunction.Then,thetheoryofrandomprocessesandtheircharacterizationbyautocorrelationandpowerspectraldensityfunctionsisdeveloped.Thetheoryisthenappliedtothedesignofoptimumlinearsystems.