For more details on the courses, please refer to the Course Catalog
| 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 | - | 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 | 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 | - | 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. | |||||||||
| 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. | |||||||||
| AIM5029 | AI Colloquium | 1 | 2 | Major | Master/Doctor | Artificial Intelligence | - | No | |
| Thisclassprovidesbroadknowledgeaboutmanyfieldsofinformationtechnology.VarioussubjectsareselectedwhicharecurrentlyhotissuesinArtificial Intelligence andinvitedtalksaregivenabouttheselectedsubjects. | |||||||||
| AIM5033 | Multi-modal Learning | 3 | 6 | Major | Master/Doctor | Artificial Intelligence | - | No | |
| Real-word data usually contain different types of modalities. For example, images can be associated with short descriptions and in a document, some ideas are illustrated by an image even though most of the ideas are delivered by texts. This graduate course will discuss multi-modal learning techniques which integrate different types of data in a machine learning method. | |||||||||