For more details on the courses, please refer to the Course Catalog
| Code | Course Title | Credit | Learning Time | Division | Degree | Grade | Note | Language | Availability |
|---|---|---|---|---|---|---|---|---|---|
| ERP4001 | Creative Group Study | 3 | 6 | Major | Bachelor/Master | - | No | ||
| This course cultivates and supports research partnerships between our undergraduates and faculty. It offers the chance to work on cutting edge research—whether you join established research projects or pursue your own ideas. Undergraduates participate in each phase of standard research activity: developing research plans, writing proposals, conducting research, analyzing data and presenting research results in oral and written form. Projects can last for an entire semester, and many continue for a year or more. SKKU students use their CGS(Creative Group Study) experiences to become familiar with the faculty, learn about potential majors, and investigate areas of interest. They gain practical skills and knowledge they eventually apply to careers after graduation or as graduate students. | |||||||||
| ESM5043 | Tcchnology Valuation | 3 | 6 | Major | Master/Doctor | 1-4 | Industrial Engineering | - | No |
| This course treats basic concepts and methods on the evaluation of technology value. The various valuation methods including Monte Carlo method, risk adjusted NPV method, Options pricing methods, strategy as a portfolio of real options are explained in this course. | |||||||||
| ESM5044 | R＆D Project Management | 3 | 6 | Major | Master/Doctor | 1-4 | Industrial Engineering | - | No |
| This course deals with the new theories and methods on R&D project management which give students abilities to apply to business practices. Particularly, the course focuses on past future of R&D project management, project planning, project evaluation, project organization. | |||||||||
| ESM5049 | Managerial Leadership | 3 | 6 | Major | Master/Doctor | 1-4 | Industrial Engineering | - | No |
| This course examines the basic concepts of managerial leadership and some case studies in real world leadership situations. Special emphasis is placed on human aspects of organizing, motivation, CEO leadership, managerial leadership styles, key successful factors of leadership, and cases of managerial leadership. | |||||||||
| ESW4001 | Virtual Reality Theory | 3 | 6 | Major | Bachelor/Master | Computer Science and Engineering | - | No | |
| Virtual reality is an interdisciplinary next-generation medium that fuses many different areas upon computer science and engineering. This course focuses on the technological aspects of virtual reality, and deals with the fundamentals of theories, hardware/software, and its applications. The major subjects include virtual reality systems, the basics of computer graphics and stereoscopic rendering, vision/auditory/haptic perception, 3D interaction and practical implementation techniques. | |||||||||
| ESW4004 | Principles of Distributed Computing | 3 | 6 | Major | Bachelor/Master | Computer Science and Engineering | Korean | Yes | |
| A distributed system is a collection of independent networked computers that function as a single coherent system. With the advent of the fast interconnect and large datasets, a.k.a. Big Data, distributed systems are becoming more important and they are widely used in various domains including AI. The primary goal of this class is to learn key design principles of distributed systems and understand how distributed systems manage resources in a networked environment. Course topics include, but not limited to, communication protocols, processes/threads, naming, synchronization, consistency, and fault tolerance. | |||||||||
| ESW4004 | Principles of Distributed Computing | 3 | 6 | Major | Bachelor/Master | Computer Science and Engineering | Korean | Yes | |
| A distributed system is a collection of independent networked computers that function as a single coherent system. With the advent of the fast interconnect and large datasets, a.k.a. Big Data, distributed systems are becoming more important and they are widely used in various domains including AI. The primary goal of this class is to learn key design principles of distributed systems and understand how distributed systems manage resources in a networked environment. Course topics include, but not limited to, communication protocols, processes/threads, naming, synchronization, consistency, and fault tolerance. | |||||||||
| ESW4006 | Information Visualization | 3 | 6 | Major | Bachelor/Master | Computer Science and Engineering | Korean | Yes | |
| With the advances in data storing and processing technologies, the size of data humans confront is increasing at an unprecedented rate. Despite the ever-increasing data size, our perceptual and cognitive abilities stay relatively unchanged, leading to an information gap between humans and data. Information visualization provides one means of addressing such information overload, as well-designed visual representations can assist our perceptual and cognitive abilities to understand, analyze, and memorize the data. In this course, students will learn to 1) design, evaluate, and critique visualization designs, 2) comprehend the characteristics of humans' perception that underpin visualization, 3) understand novel visualization and interaction techniques, and 4) implement interactive data visualizations. The topics of this course will include but not limited to: - Foundations of Information Visualization, Exploratory Data Analysis (EDA), Visual Analytics - Data and Task Abstraction - Mark, Channel, Color, Perception, Interaction, and Animation - Tables, Maps, Networks, Text, and Uncertainty - Visualization for Large-scale and High-dimensional Data - Visualization for the Explainability and Trustworthiness of Machine Learning Methods | |||||||||
| ESW4007 | Principles of Compilers and Programming Languages | 3 | 6 | Major | Bachelor/Master | Computer Science and Engineering | Korean | Yes | |
| This is a graduate level course for compiler and programming language technology. Based on data flow analysis and control flow analysis, techniques for static/dynamic analysis, compiler optimizations, and code generation are explored. In addition, dependence analysis and loop transformation, which are base techniques for auto-parallelization to support multicore computing and vector processing, are covered. | |||||||||
| ESW4008 | Data Science and Security | 3 | 6 | Major | Bachelor/Master | Computer Science and Engineering | - | No | |
| This course is to learn about the AI security and privacy. Additionally, we study the role of AI, data and data analytics for security and privacy applications. This course focuses on applications of AI, machine learning and big data analytics to various security and privacy problems, using various data analysis and AI techniques to solve challenging security and privacy issues. | |||||||||
| ESW4008 | Data Science and Security | 3 | 6 | Major | Bachelor/Master | Computer Science and Engineering | - | No | |
| This course is to learn about the AI security and privacy. Additionally, we study the role of AI, data and data analytics for security and privacy applications. This course focuses on applications of AI, machine learning and big data analytics to various security and privacy problems, using various data analysis and AI techniques to solve challenging security and privacy issues. | |||||||||
| ESW4009 | Blockchain and Smart Contract | 3 | 6 | Major | Bachelor/Master | Computer Science and Engineering | - | No | |
| This course introduces blockchain and smart contract technologies that enable peer-to-peer transfer of digital assets without any intermediaries. We specifically aim to provide students with (1) an understanding and working knowledge of foundational blockchain concepts, (2) programming skills for designing and implementing smart contracts, (3) methods for developing decentralized applications on the blockchain, and (4) information about the ongoing specific industry-wide blockchain frameworks. The course also covers a range of essential topics, from the cryptographic underpinnings of blockchain technology to enabling decentralized applications on blockchain platforms. | |||||||||
| ESW4013 | Automated Software Analysis | 3 | 6 | Major | Bachelor/Master | Computer Science and Engineering | - | No | |
| The class teaches both foundation and practical aspects of automated software analysis, which is used by various research domains such as software engineering, program analysis, and security. Such techniques are also increasingly adopted in industries in order to automatically detect SW bugs and vulnerabilities before officially releasing the products. In this course, the foundation of automated software analysis techniques such as the theory of abstract interpretation, data-flow analysis, concolic testing, symbolic execution, fuzzing, and instrumentation will be introduced. Moreover, the recent research papers from top-tier conferences which utilizes such automated analysis techniques will be introduced in order to teach students how these techniques can be used to solve research problems. In addition, through assignments, students will design and implement practical software analysis tools that find bugs and verify software properties. After taking this course, students are able to: - Understand the foundation and practice of software analysis techniques, and - Able to detect SW bugs and vulnerabilities automatically, and - Could perform research on automated SW analysis | |||||||||
| ESW4013 | Automated Software Analysis | 3 | 6 | Major | Bachelor/Master | Computer Science and Engineering | - | No | |
| The class teaches both foundation and practical aspects of automated software analysis, which is used by various research domains such as software engineering, program analysis, and security. Such techniques are also increasingly adopted in industries in order to automatically detect SW bugs and vulnerabilities before officially releasing the products. In this course, the foundation of automated software analysis techniques such as the theory of abstract interpretation, data-flow analysis, concolic testing, symbolic execution, fuzzing, and instrumentation will be introduced. Moreover, the recent research papers from top-tier conferences which utilizes such automated analysis techniques will be introduced in order to teach students how these techniques can be used to solve research problems. In addition, through assignments, students will design and implement practical software analysis tools that find bugs and verify software properties. After taking this course, students are able to: - Understand the foundation and practice of software analysis techniques, and - Able to detect SW bugs and vulnerabilities automatically, and - Could perform research on automated SW analysis | |||||||||
| ESW4014 | Principles of Reinforcement Learning | 3 | 6 | Major | Bachelor/Master | Computer Science and Engineering | Korean | Yes | |
| In this course, students learn the basic theory algorithm of Reinforcement Learning (RL) to find the optimal policy for a given environment. From basic reinforcement learning theories such as Markov Decision Process, Planning, and Q-learning to deep neural network-based reinforcement algorithms such as Value Function Approximations and Policy Gradient Methods. In addition, Model-based RL through estimating environments, Exploitation & Exploration Trade-off, and Inverse RL that mimics the behavior of experts are also covered. Basic knowledge of data structures, algorithms and machine learning is required to take this course. | |||||||||