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Graduate

Department of Immersive Media 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.
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 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.
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.
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.
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.
AIM5015 Theory of Embedded Systems 3 6 Major Master/Doctor 1-4 Artificial Intelligence - No
Thiscourseintroducestheessenceofembeddedsoftwareandprogrammingskillsforembeddedsystemdesign.Itcoversthesubjectsondatastructureandsystemprogramming,embeddedsystemprogrammingenvironment,overviewofrealtimeOS,taskandscheduling,synchronizationandcommunication,linuxdriverdevelopmentenvironment,andlinuxdevicedriverprogramming.
AIM5019 Theory of Speech Recognition 3 6 Major Master/Doctor 1-4 Artificial Intelligence - No
Thislessonconsidersspeechrecognitionbasedonpatternrecognition.Mainsubjectsarenatureofspeechsounds,principlesofspeechanalysis,fundamentalsofspeechrecognition,dynamictimewarping(DTW),hiddenmarkovmodel(HMM),neuralnetwork,robustnessinspeechrecognition,andspeechsynthesis.
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.
AIM5023 Data Mining Theory and applications 3 6 Major Master/Doctor 1-4 Artificial Intelligence - 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 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.