For more details on the courses, please refer to the Course Catalog
| Code | Course Title | Credit | Learning Time | Division | Degree | Grade | Note | Language | Availability |
|---|---|---|---|---|---|---|---|---|---|
| ROB5008 | Parametric/Nonparametric Bayesian | 3 | 6 | Major | Master/Doctor | 1-8 | Intelligent Robotics | - | No |
| This course explores basic parametric Bayesian inference, data analysis, pattern recognition, and nonparametric Bayesian modeling and inference techniques from a mathematical perspective, all aimed at enhancing the cognitive intelligence of robot system. This course provides an understanding of both parametric and nonparametric Bayesian concepts, by covering fundamental cognitive data processing techniques such as PCA, LDA, and kernel trick-based SVM, Bayesian Decision Theory, Density Estimation, maximum a posteriori (MAP), Bayesian Learning, Gaussian Mixture Model (GMM), Expectation-Maximization, Variational inference, VAE, GAN, and Bayesian deep learning considering uncertainty. The goal of this lecture is to achieve cognitive intelligence in robot system by modeling the distribution of data perceived by the robot using parametric and nonparametric Bayesian methods. | |||||||||
| ROB5009 | Estimation and Decision Theory | 3 | 6 | Major | Master/Doctor | 1-8 | Intelligent Robotics | Korean | Yes |
| This course introduces the classical and modern topics in estimation and decision theory for robot localization, environmental perception, and control. Basic Bayesian estimation are discussed for probability distribution estimation for robot system such as robot positions and observed environments. Topics include minimum variance unbiased estimator, the Cramer-Rao lower bound, sufficient statistics, best linear unbiased estimators, maximum likelihood estimator (MLE), least squares, exponential family, multivariate Gaussian distribution, minimum mean square error (MMSE), maximum a posteriori (MAP), linear MMSE, sequential linear MMSE, Bayesian filtering, generalized Bayesian filters, nonlinear filters, data association, and Gaussian process regression, all of which are essential for optimal probability modeling and inference for robot system. | |||||||||
| ROB5014 | Mathematics and Simulation for Robotics | 3 | 6 | Major | Master/Doctor | 1-8 | Intelligent Robotics | Korean | Yes |
| This course deals with mathematical fundamentals for intelligent robotics such as linear algebra, probability, statistics, optimization for robotics. The mathematics concept is implemented in computer programming. ROS, robotic simulators such as Isaac Sim is covered. Robotic kinematics is practiced with robot simulation. | |||||||||
| ROB5015 | Aerial Robotics | 3 | 6 | Major | Master/Doctor | 1-8 | Intelligent Robotics | Korean | Yes |
| In this course, students will learn about the fundamental principles of fixed-wing and rotary-wing flight and explore the operational principles and potential applications of various flying robots. Furthermore, the course covers the study of multimodal mobile robots capable of both flight and other forms of movement, and examines the potential application of key aerial robotics technologies in the development of future aircraft. | |||||||||
| SFC7001 | Smart Factory Convergence Capstone Design 1 | 3 | 6 | Major | Bachelor/Master/Doctor | Smart Factory Convergence | Korean | Yes | |
| This corporate-sponsored projects course in smart factory is an industry-university partnership that integrates design, manufacturing, service engineering, and business realities into the engineering curriculum. Students take their project ideas from concept to reality by designing, prototyping, and simulating real solutions in state-of-the-art facilities. This course challenges students to apply the knowledge and tools acquired during their undergraduate education to solve real-world engineering problems. | |||||||||
| SFC7002 | Smart Factory Convergence Capstone Design 2 | 3 | 6 | Major | Bachelor/Master/Doctor | Smart Factory Convergence | Korean | Yes | |
| This course provides a unique opportunity for industry to partner with our university to educate the next generation of world-class engineers. Interdisciplinary teams of students work together to tackle projects sponsored by industrial clients. These teams collaborate with engineering faculty, who serve as mentors and advisers, to devise ideas to solve engineering problems. | |||||||||
| SNT5043 | Introduction to nanoparticle engineering | 3 | 6 | Major | Master/Doctor | 1-8 | Nano Science and Technology | English | Yes |
| As a basis for nanoparticle research, this course explains the characteristics of aerosol/hydrosol suspended in gas/liquid and analyze the physical, chemical, electrical, and optical properties of nanoparticles. Major application fields include envirionment contamination, basic sciences such as the glow of the setting sun, lightning, environmental fluid machinary and industry-related powder process, semiconductor and semiconductor equipment, clean room, power plant. Course contents: particle engineering introduction and application, fluid properties, particle motion, particle size statistics, inertial transport, brownian motion/diffusion, thermophoresis, electrical property, optical property, nanoparticle measurement and analysis methods, particle sampling, coagulation, condensation and evaporation | |||||||||
| SWE2016 | Algorithms | 3 | 6 | Major | Bachelor | 2 | Computer Science and Engineering | Korean,English,Korean | Yes |
| The purpose of this course is to introduce algorithms for solving problems in computer applications and basic principles and techniques for analyzing algorithms. The topics will include analyzing criteria, searching, sorting, graphs, polynomials, string matching, and hard problems etc. | |||||||||
| SWE3011 | Introduction to Artificial Intelligence | 3 | 6 | Major | Bachelor | 4 | Computer Science and Engineering | English,Korean | Yes |
| This course focuses on foundation of theory and introduction of advanced topics. Detailed subjects for theory are problem representation in state space, search strategy including breadth first search, depth first search and heuristic searchand knowledge representation methods such as using predicate logic, resolution and using rules. Advanced topics planning system (STRIPS), neural network and fuzzy techniques such as perceptron and hopfield network with learning methods, computer vision techniques such as image representation, edge detection, line and curve detection are also introduced. Finally, we introduce symbolic programming language, LISP with examples. | |||||||||