For more details on the courses, please refer to the Course Catalog
| Code | Course Title | Credit | Learning Time | Division | Degree | Grade | Note | Language | Availability |
|---|---|---|---|---|---|---|---|---|---|
| AIM5043 | AI Accelerator | 3 | 6 | Major | Master/Doctor | - | No | ||
| Current artificial intelligence hardware architecture is based on the artificial deep neural network model, but requires more innovative structures similar to the human brain and nervous system. Based on the conventional AI accelerator architecture optimized for matrix computation, we explore a variety of next-generation architectures that encompass analog-based neural network circuitry, processing-in-memory, and beyond the von Neumann architecture. | |||||||||
| AIM5044 | Neuromorphic Processor | 3 | 6 | Major | Master/Doctor | - | No | ||
| The purpose of the Neuromorphic Processor is to learn a variety of techniques for designing neuromorphic systems mimicking the structure and behavior of a biological brain. After understanding the structure and behavior of a biological brain, the students will learn about the basic properties of memristive devices and CMOS circuits as important tools to implement neuromorphic systems. The lecture also deals with the architectures and operation principles of current neuromorphic processors, and learns how to design low-power high-performance neuromorphic processors. | |||||||||
| AIM5045 | Edge Computing | 3 | 6 | Major | Master/Doctor | - | No | ||
| In this course we will study the extension of cloud computing to today’s edge computing and learn how they can be leveraged in a combined edge-cloud- environment. We will begin with a review of current cloud computing environment and the structure of data center networking. We will then learn about the definition of edge computing and the complementary nature of cloud computing and edge computing. It also covers the difference between device edge and cloud edge depending on computing over edge devices and cloud. We will study edge architecture, coordination of cloud services, Apache Edgent, geo-distributed computing, machine learning in edge computing and algorithms for distributed big data analytics. | |||||||||
| AIM5046 | SW-HW Integrated Projects | 3 | 6 | Major | Master/Doctor | - | No | ||
| In SW-HW Integrated Projects course, practical design projects will be performed based on the basic theory studied through various courses including SW-HW Integrated Design Methodology. In this course, AI related design project subjects will be determined considering the data processing speed, power consumption and AI system with optimum performance will be designed through the optimum partitioning between SW and HW and Co-Design to meet the project targets. Design results by SW-HW Co-Design will be compared with those by SW standalone and HW standalone approaches. | |||||||||
| AIM5047 | Problem-Solving R&D Projects | 3 | 6 | Major | Master/Doctor | - | No | ||
| This course is designed to provide students with an opportunity to understand AI-related industries and familiarize themselves with issues that are being solved in the industry, and to develop their ability to solve the problems by themselves. The contents of the class are selected by the project as the problem that arises in the industrial field and a new idea is required. | |||||||||
| AIM5048 | Startup-Linked Capstone Projects | 3 | 6 | Major | Master/Doctor | - | No | ||
| ThepurposeoftheStartup-Linked CapstoneProjectisforthestudentstoapplytheoreticalknowledgeacquiredduringtheDataScienceprogramtoaprojectinvolvingactualdatainarealisticsetting for start-up.Thiscourserunsaprojecttosolveactualproblemsfromstartup industry.Subjectsfortheprojectswill be fromspecificdomainsinactual situations.Duringtheproject,studentsengageintheentireprocessofsolvingareal-worlddatascienceproject,fromcollectingandprocessingactualdatatoapplyingsuitableandappropriateanalyticmethodstotheproblem.Boththeproblemstatementsfortheprojectassignmentsandthedatasetsoriginatefromreal-worlddomains in start-up industry. | |||||||||
| AIM5049 | AI Healthcare System | 3 | 6 | Major | Master/Doctor | - | No | ||
| Modern healthcare system consists of many interlinked complex components. Managing them properly is a key issue in effective healthcare delivery. This course deals with components in the healthcare system and how to manage them using artificial intelligence-based operations management tools. Major topics include design of healthcare capacity, optimization of healthcare facility locations, supply chain management of blood, healthcare information system, and organ distribution models. | |||||||||
| AIM5050 | Medical Image Analysis | 3 | 6 | Major | Master/Doctor | - | No | ||
| Modern healthcare involves increased use of medical imaging and thus it is important to analyze medical imaging data for better healthcare. This course explains various medical imaging modalities and deals with how to extract clinically relevant information from medical imaging using image processing/computer vision techniques. Major topics include principles of magnetic resonance imaging and computed tomography, filter-based feature extraction, and neural network-based feature extraction. | |||||||||
| AIM5051 | AI Business Platform | 3 | 6 | Major | Master/Doctor | - | No | ||
| This course aims to study diverse business models based on AI and to study platform for constructing AI business ecosystem. In other words, this course studies the system (environment) where various complementary interactions can occur in the ecosystem of business, service, or technology through AI technology development. Through this course, a platform in which AI developers, analysts, providers, and users can coexist in a virtuous circle can be constructed. | |||||||||
| AIM5052 | Manufacturing Bigdata Analysis | 3 | 6 | Major | Master/Doctor | - | No | ||
| This course is aim to understand the big data characteristics of the manufacturing domain and to learn the big data analytics capability in the manufacturing field. First, we will learn about the importance of manufacturing big data by approaching from a business point of view, and then discuss various computing technologies and software platforms which can deal with manufacturing big data. This course deals with the understanding of statistical language R and basic statistics. It also analyzes how to control big data by explaining efficient data extraction, data analytics and data visualization. It also covers logistic regression, LDA, clustering algorithms, time-series analysis, SVM/KNN. We will explore techniques for handling big data using Hadoop or Spark, which are recently attracting attention as a platform for processing big data. We will learn about production process in manufacturing and look at production management and quality control based on big data analytics. | |||||||||
| AIM5053 | AI and Ethics | 3 | 6 | Major | Master/Doctor | - | No | ||
| In this course, students will analyze AI models' limitations in terms of ethics and learn how to overcome the limitations. Every technology has an intended use and unintended consequences. For example, nuclear power makes power plants and atomic bombs. AI also has this dual-use. Students will learn the problem of dual-use in AI and understand and suggest solutions. | |||||||||
| AIM5054 | Computational Social Science | 3 | 6 | Major | Master/Doctor | - | No | ||
| In this course, students will learn computational social science (CSS). CSS is the intersection of computer science, statistics, and social science. It solves the research problem in social science by computational methods, especially AI. Students will learn the research problems and AI models in CSS. | |||||||||
| AIM5055 | Bayesian learning | 3 | 6 | Major | Master/Doctor | - | No | ||
| In this course, students will learn Bayesian learning. Bayesian statistical methods help estimate probabilities and make decisions under uncertainty. And Bayesian Deep Learning, which combines the benefits of modern deep learning methods and modern Bayesian statistical methods, is emerging. Students will learn Bayesian learning at first and learn the Bayesian Deep Learning in more detail. | |||||||||
| AIM5056 | Machine learning with Graphs | 3 | 6 | Major | Master/Doctor | English | Yes | ||
| Machine learning with graphs is a quickly growing subfield of machine learning that seeks to apply machine learning methods to graph-structured data. Applications of machine learning on graphs include drug design, user profiling, and friendship recommendation in social networks. This course will provide an introduction to graph representation learning, including matrix factorization-based methods, random-walk based algorithms, and graph neural networks. During the course, we will study both the theoretical motivations and practical applications of these methods. | |||||||||
| AIM5057 | Knowledge Graph-based Methods | 3 | 6 | Major | Master/Doctor | - | No | ||
| This course is to learn foundations, techniques, and algorithms for building and leveraging knowledge graphs. Students will study the theory and applications of the techniques needed to build and query massive knowledge graphs. Topics include crawling web sites, wrapper learning, information extraction, source alignment, string matching, entity linking, graph databases, querying knowledge graphs, data cleaning, Semantic Web, linked data, graph analytics, and intellectual property. | |||||||||