Korea, South

This course covers fundamental stochastic models of probabilistic phenomena, including conditional probability, stochastic processes, Markov chains, properties and applications of Markov chains, Poisson processes, renewal processes, and martingales. Topics include Conditional Expectation, Martingales in Discrete Time, Optional Stopping Theorem, Martingale Inequalities, Convergence and Uniform Integrability, Markov Chains, Long-Time Behavior of Markov Chains, Poisson Process, Brownian Motion, and Stochastic Differential Equations.

This course covers the principles of relationship marketing. Students are introduced to strategic and tactical issues involved in building and managing relationships with customers. The course also deals with analytical methods for identifying customer needs, calculating customer lifetime value, making targeting decisions, and evaluating the impacts of marketing activities. Emphasis is placed on the implementation of the methods using software tools. Topics include Marketing math, Analyzing customer data, Identifying customer needs & segmentation, Evaluating the impacts of marketing activities, Utilizing transaction data, Making targeting decisions, Customer retention, Customer lifetime value, and Relationship marketing in digital environments.

This course prepares students to enter the ever-evolving 'world of work' by exploring the dynamics that influence current and future employment trends and how to respond to these evolutions through self-adaptation. Students explore the socio-economic and business environment that futurists anticipate for the next decade and develop career strategies to address the challenges posed by the rapidly evolving work environment. Students acquire the necessary skills in interpersonal communication, relationships, group discussions, and presentations to effectively respond to the demands of a more fluid and dynamic global work environment. 

The course is more than a language course as it also explores socio-pragmatic competence in professional settings through development of discourse analysis and strategies, identification of both general and specific needs and requirements in given and ever-evolving situations and development of constructive approaches to satisfying those needs based in part on multicultural perspective and sensitivity. 

This course introduces the study of meaning. With the rapid rise in practical applications of artificial intelligence systems, it is now more crucial than ever for us to define what it means to be human, and there is nothing more humanistic than studying the concept of meaning itself. 

Students engage with some of the most influential scientific, literary, and philosophical texts that have shaped the world today with the objective to move beyond a passive understanding. Students are challenged to think critically and actively about how the ideas put forth in these texts have come to be rejected, revised, and/or replaced, and how this very process of the shifting dominant narrative of meaning (i.e., not just the works by themselves in isolation) continues to influence the society and culture that they currently live in. 

Topics include Writing systems, Rhetoric, Similes, metaphors, and meaning, Creating new knowledge via logic, What is knowledge, Are signs arbitrary, Meaning as behavior, Language and thought Sarcasm, Mathematical meaning (axiomatic system), Mathematical meaning (axiomatic system), Can computers and AI understand meaning, Society and language use, How might aliens define meaning. 

This course covers the tools and systems used to implement cloud computing systems, and presents key issues to be addressed, such as virtualization. Students learn cloud system platform technologies and detailed component technologies then configure servers and perform programming on public clouds like Amazon Cloud System (AWS) or Google Cloud System. 

Topics include Cloud computing concepts, Cloud computing models, Cloud computing architecture, Cloud computing platforms, Virtualization, Synchronization, Coordination, Distributed deadlock. 

This advanced course covers the dynamic interactions between humans and technology. Specifically, we trace the evolution of computer-mediated communication (CMC), explore impression formation, identity, and well-being online, and extend into human–machine communication (HMC) with AI, social robots, and algorithmic media. Students critically examine theories, research, and ethical issues shaping the future of communication. Students should expect to do extensive research and produce a research paper and final paper presentation.  

Topics include Computer-mediated communication, Impression formation and relationship development, Communication and self, Psychological well-being and social support, Merging mass and interpersonal communication via interactive communication technology, Are computers social actors?   

This course covers machine learning techniques to analyze visual data. Specifically, this course focuses on fundamental machine learning and recent deep learning methods that are widely used in visual data analysis and discusses how these methods are applied to solve various problems with visual data. This course consists of lectures, practices, and projects. 

Topics include Introduction to CV/DL, Convolutional neural networks, Training, optimization, data, Few-shot learning, Object detection and segmentation, RNNS, Domain adaptation, Multimodal learning, Deployment. 

Prerequisite: Basic knowledge of Python 

This course develops and interprets the mathematical foundations of statistics including theories of stochastic variables, variable transformations, sample distribution, estimation, and hypothesis testing. 

Students explore calculus and probability theory necessary to analyze data and draw statistical inferences about the population. Thus, there is a major focus on deriving statistical estimators which are functions of data and a focus on studying their statistical properties. The course covers point and interval estimation methods which are widely used in academia and industry. After establishing statistical procedures to obtain inference about the population, we apply them to real problems by using Excel. If time permits, we will talk about linear regression models and Bayesian methods, which are both essential in quantitative finance, actuarial science, medical science, etc. Please note that the lectures are in-depth regarding mathematical proofs of theorems in the textbook; students should expect the class to be theoretical and rigorous. 

Prerequisites: An undergraduate level understanding of calculus and probability. Students should have a solid understanding of integral with one variable (calculus with two variables will be very helpful for advanced topics) and integral by parts.  

This course provides an introduction to research methodology with an emphasis on experimentation. The goal of this course is to teach students how to turn an idea into a good research question and then turn that question into rigorous research studies. To do so, we survey a variety of basic and advanced research techniques, including experimental, behavioral, observational, survey, and physiological methods. Students participate in discussions to understand the applications of each class topic to their research interests. Finally, students design their own studies that utilize methodological approaches.  

Topics include Having and testing ideas, Operationalization and issues of validity, Statistical power and correlational design: measurement construction, Experimental design, Repeated sampling, Survey, Unobtrusive measures and observation, Inducing and assessing emotions, Physiological methods, Dyadic and group designs, Meta-analysis and cross-cultural research, Presenting and publishing research.