Korea, South

This course covers the fundamentals of mechanical design for devices and systems, including an examination of economic and manufacturing viability. 

Students will learn various design approaches for real engineering problems and, through team and individual projects, will participate in an entire design process from a sketch to a performance test. At the end of the course a contest will be held as a performance test for designed products.   

Topics include fostering creative mechanical design skills, fostering creative implementation skills of product design, collaboration and teamwork skills, concept design, 2D and 3D design, machining and manufacturing skills, and how to create an effective presentation. 

The context and behaviors of computer usage have been rapidly changing as the shift of computer use environments moved from desktop computers to mobile devices to the Internet of Things, leading to the remarkable appearance of Web 3.0 technologies including XR/AI/Sensor/Blockchain which have been called new design frameworks of NPD processes that may support the big step from “interacting with computers” to “interacting with AI.”  

Now advanced enterprise architectures are rapidly adopting the “Cognitive Internet of Things.” This drastic shift signifies a fundamental game changer for the UX matters.  

This course converts the initiative in the design management strategy into the study of new HCI/UX design and analysis methods in relation to cognitive science theories and design methodologies, bridging contemporary and in-depth academic interests and approaches in the field. 

Students will explore the theoretical framework of human-computer interaction (HCI) and will build an understanding of HCI-based UX research methodology, the user research process, and practical methodologies, and will engage with the current topics of HCI/UX research and the practical use of convergence studies. 

This course develops basic volleyball skills, game playing skills, and a knowledge of the rules and practice of volleyball.

This introductory Python algorithms course is designed for beginners in Python programming. Conducted online, this course excludes difficult mathematics and complex code, allowing students to directly code and debug basic algorithms. The course will be conducted slowly and in detail to accommodate coding novices. 

This course explores the concept and process of design thinking. Students will gain awareness of their own process, develop research skills and methods necessary for any design project, both in academia and in the commercial world, and examine methods for projecting plausible futures based on current trends. 

The course introduces the history and development of thoughts on design as a discipline and important concepts that have significantly contributed to design research and studies. Students will encounter curated design thoughts and assignments to help develop their own perspectives on design and produce useful/usable work pieces for their career.  

This course primarily focuses on economic analysis in terms of welfare. Topics include how to evaluate market allocations based on efficiency, how to achieve efficient allocations through the market, and when the market fails in achieving efficient allocations.  

Students will study market structures besides the competitive market such as standard monopoly (uniform pricing), monopoly behavior (price discrimination) and oligopoly (basic concepts in game theory are also covered).  

Additionally, the course will consider exchange, production, welfare, social choice (e.g., an investigation of voting rules), and externalities (If time allows).  

This course emphasizes the development of microeconomic models to analyze economic decision-making of agents and provides students with the basic toolkit of microeconomic theory in preparation for advanced further coursework.  

Prerequisites: Principles of Microeconomics, Basic calculus 

This course examines major topics in pattern recognition, particularly aspects of classification and decision. Students will gain effective pattern recognition tools with which to analyze the often vast amounts of diverse data in research applications. 

Topics include introduction to pattern recognition - machine perception - PR systems and design cycle, Bayesian decision theory for continuous features - Bayes Decision Rule - minimum-error-rate classification - classifiers, normal density, discriminant functions and discrete Bayesian decision theory - discriminant functions for the normal density - error probabilities and integrals - Bayes Decision Theory for discrete features, maximum-likelihood and Bayesian parameter estimation - Bayesian parameter estimation: Gaussian case - Bayesian parameter estimation: general theory - HMM, nonparametric techniques - density estimation - Parzen windows - nearest neighbor estimation (NN, k-NN) - fuzzy classification, linear discriminant functions i - linear discriminant functions and decision surfaces - generalized linear discriminant functions - minimizing the perceptron criterion function, relaxation procedures, linear discriminant functions ii - minimum square-error procedures - relation to Fishers linear discriminant - the Widrow-Hoff and Ho-Kashyap procedures - multicategory generalizations - ridge regression and its dual form [2] - classification error based method [2], model assessment and performance evaluation - bias, variance and model complexity [2] - model assessment and selection 2] - confusion matrix, error rates, and ROC [2] - statistical inference [2] - statistical errors [2], dimension reduction and feature extraction - principal component analysis - Fisher linear discriminant - nonlinear projections, support vector machines - introduction - SVM for pattern recognition [2] - linear support vector machines [2] - nonlinear support vector machines [2], multilayer neural networks - introduction - feedforward operation and classification - backpropagation algorithm - some issues in training neural networks - key ideas in classification, introduction to deep learning networks - convolutional neural networks (CNN) - autoencoders - deep belief networks - deep reinforcement learning - generative adversarial networks (GAN), algorithm-independent machine learning - introduction - bias and variance - resampling for classifier design - estimating and comparing classifiers - combining classifiers, unsupervised learning and clustering - mixture densities and identifiability - maximum-likelihood estimates - application to normal mixtures. 

Prerequisites: Linear Algebra, Probability, MATLAB, Python, or C-Programming Skills 

This course covers basic knowledge of modern biology that students studying natural sciences must have with an emphasis on life phenomena from a molecular interpretation. 

Topics include hormones, sensory organs, integration and coordination of the nervous system, movement, classification of organisms, ecology, and behavior. 

This course encompasses analyses of the psychological impact of media content and presentation. The courses provides an understanding of how individuals process media contents as well as how the media affects individuals’ knowledge, attitudes, and behaviors. A variety of topics such as the psychological processing of information, media violence, sexual content, stereotyping, and the effects of new communication technologies are covered. 

This course covers the mechanics of rigid and deformable solids in equilibrium and is a continuation of the material introduced in Solid Mechanics 1. Students will learn how to apply fundamental physical considerations which govern the mechanics of solids in equilibrium to solve any engineering problems such as beam deflection, torsion, buckling etc. Topics include: Review from Solid Mechanics l; transverse shear; combined loading; stress transformation; strain transformation; deflection of beams and shafts; buckling of columns; energy methods.