For more details on the courses, please refer to the Course Catalog
| Code | Course Title | Credit | Learning Time | Division | Degree | Grade | Note | Language | Availability |
|---|---|---|---|---|---|---|---|---|---|
| AIM5011 | Numerical Analysis for AI | 3 | 6 | Major | Master/Doctor | 1-4 | - | 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 | - | 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 | - | No | |
| Theaimofthiscourseistodevelopathoroughunderstandingoftheprinciplesofrandomprocessesandknowledgeofapplyingthemtosomeimportant problems.First,thebasictheoryinprobabilityandrandomprocessisintroduced,payingparticularattentiontothemultivariateGaussiandensityfunction.Then,thetheoryofrandomprocessesandtheircharacterizationbyautocorrelationandpowerspectraldensityfunctionsisdeveloped.Thetheoryisthenappliedtothedesignofoptimumlinearsystems. | |||||||||
| 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. | |||||||||
| AIM5015 | Theory of Embedded Systems | 3 | 6 | Major | Master/Doctor | 1-4 | - | No | |
| Thiscourseintroducestheessenceofembeddedsoftwareandprogrammingskillsforembeddedsystemdesign.Itcoversthesubjectsondatastructureandsystemprogramming,embeddedsystemprogrammingenvironment,overviewofrealtimeOS,taskandscheduling,synchronizationandcommunication,linuxdriverdevelopmentenvironment,andlinuxdevicedriverprogramming. | |||||||||
| AIM5016 | Advanced Computer Architectures | 3 | 6 | Major | Master/Doctor | 1-4 | - | No | |
| Thefocusofthecoursewillbeonhigh-performanceprocessorandmemoryarchitectures.Wewillexplorevarioustechniquesdesignedtomaximizeparallelismandimproveperformance.Wewilllookattheinfluenceoftechnologyonprocessorandmemoryarchitecturesandhowthatmayaffectfutureprocessordesigns.Theemphasisisonthemajorcomponentsubsystemsofhighperformancecomputers:pipelining,instructionlevelparallelism,memoryhierarchies,input/output,andnetwork-orientedinterconnections.Studentswillundertakeamajorcomputingsystemanalysisanditsrelatedproject. | |||||||||
| AIM5017 | NPU Design | 3 | 6 | Major | Master/Doctor | 1-4 | - | No | |
| Recently, the development of artificial intelligence technology has increased the necessity of highly efficient neural network processor, and neural processing unit (NPU) can be implemented as standalone single chip or multiprocessor system-on-chip (MPSoC). In this course, basic knowledge of integrated circuit, semiconductor technology, and computer architecture is included and oriented to high efficiency NPU design methodology optimized in performance, area, and power efficiency according to the evolution of artificial intelligence technology. | |||||||||
| AIM5018 | Theory of Analog IC Design | 3 | 6 | Major | Master/Doctor | 1-4 | - | No | |
| ThiscourseprovideasimulationtechniqueandCMOSdevicemodelingforanalogdesign.Basedonthebasicdesigntechnique,thecoursecoverthefollowingsubjectsformemorydesign,CurrentMirrorCircuit,OP-Ampdesign,ReferenceCircuitDesign,ChargePumpDesign,PLL/DLLdesignandI/OBufferdesign. | |||||||||
| AIM5019 | Theory of Speech Recognition | 3 | 6 | Major | Master/Doctor | 1-4 | - | No | |
| Thislessonconsidersspeechrecognitionbasedonpatternrecognition.Mainsubjectsarenatureofspeechsounds,principlesofspeechanalysis,fundamentalsofspeechrecognition,dynamictimewarping(DTW),hiddenmarkovmodel(HMM),neuralnetwork,robustnessinspeechrecognition,andspeechsynthesis. | |||||||||
| AIM5020 | Theory of Computer Vision | 3 | 6 | Major | Master/Doctor | 1-4 | 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 | 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 | - | 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. | |||||||||
| AIM5023 | Data Mining Theory and applications | 3 | 6 | Major | Master/Doctor | 1-4 | - | No | |
| Data mining is the process of discovering interesting patterns and relationships in massive data sets. This graduate course will focus on discussing the state-of-the-art data mining techniques which are recently published works at top-tier conferences. Not only the traditional data mining techniques which are basically designed to handle structured data but also more advanced tools/methods for handling unstructured data (e.g., graphs, images, and texts) will be discussed. | |||||||||
| AIM5024 | Recommendation Systems | 3 | 6 | Major | Master/Doctor | 1-4 | - | 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 | - | No | |
| Inordertouserobotsveryefficiently,robotsarerequestedtobeabletoperformalltasksashumanscan.Thiscoursediscussesthetechniqueofsensoranditsapplicationinordertomakerobotsperformtasksintelligently. | |||||||||