Courses (@SNU)

Undergraduate Courses

406.432* Statistics for Industrial Engineering (U)
The primary objective of the course is to improve the ability of thinking quantitatively and systematically, and the ability of dealing with problems in management, information, communication, and engineering systems.

406.327 Mathematical Methods for Industrial and Management Engineering (U)
This course introduces basic theories and scientific computing skills on mathematical methods for industrial engineers. In terms of methodology, the course covers such subjects as matrix computations, differential equations, Fourier transform, and MCMC. This course also emphasizes mathematical and computational practices for practical problems in industrial and management engineering using MATLAB or R programming.

Graduate Courses

406.659* Applied Multivatiate Statistical Analysis (G)
The primary objective of the course is to provide students with fundamental understanding and embodied knowledge on multivariate data analysis in the field of industrial engineering, management, sciences, and engineering. To this end, this course introduces multivariate statistical models such as regression, dimension reduction, clustering, classification, statistical learning models, and makes students intelligent users of these models by applying them and interpreting the results.

406.546* Data Mining Techniques (G)
The primary objective of the course is to cover in-depth theory and methods for fundamental data mining and artificial intelligence techniques and to discuss recent research topics on data mining and text mining applications.

406.805 Advanced Topics In Statistical Learning (G)
The primary objective of the course is to cover in-depth theory and algorithms for recent probabilistic graphical models for decision support under uncertainty and to discuss recent research topics in statistical inference as well as in application areas

406.568 Special Topics in Probability Models (G)
The primary objective of the course is to present probability models and arbitrage theory in continuous time stochastic process using the modern martingale approach and to apply them to financial models. Technicalities are given relatively little emphasis so as to simplify these concepts and to make plain the similarities among continuous time probability models.

Archive

406.317* Operations Research 2 (U)
The primary objective of the course is to improve the ability of thinking quantitatively and systematically, and the ability of dealing with problems in management, information, communication, and engineering systems.

406.311 Simulation (U)
Simulation refers to a broad collection of methods and applications to mimic the behavior of real systems, usually on a computer with appropriate software. This is intended as a first course in simulation which starts with the basic concepts of simulation and covers the elementary skills and knowledge required to conduct independent simulation studies. The R, Matlab, and other software such as ARENA will be used in class in order to provide the students with a hands-on experience to model and analyze the behavior of various systems.

406.567 Numerical Optimization for Statistical Learning (G)
The primary objective of the course is to introduce the interplay between optimization and statistical learning for designing efficient algorithms for big data mining. To this end, this course covers recent large-scale numerical optimization techniques such as trust-region methods, conjugate gradient methods, quasi-Newton Methods, SQP, proximal methods, first-order methods, and so on, seasoned with demonstrations using MATLAB.

Other Courses (@Postech)
(IE201) Engineering Economy (U)
To provide students with a sound understanding of the principles, basic concepts, and methodology of engineering economy, and to help them develop proficiency with these amethods and with the process for making rational decisions regarding situations they are likely to encounter in professional practice, and also to learn the fundamental concepts of risk and return used in finance today, including mean-variance portfolio theory, capital asset pricing model.
(IE281) Computer Applications in Industrial Engineering (U)
To learn a objected-oriented programming such as C++ language and the fundamentals of data structures and computer algorithms, and apply them to solve industrial engineering problems.
(IENG491A) Linear Engineering (U)
To provide a unified treatment of fundamental linear problems such as linear equations, linear regression (linear least squares, linear mean squares), and linear systems, seasoned with demonstrations using MATLAB.
(IENG486) Financial Engineering (U)
To learn important mathematical models used in finance today, including cash flows, mean-variance portfolio theory, capital asset pricing model, derivative securities, and basic options theory.
(ME&IE 683) Advanced Topics in Artificail Intelligence (G)
To learn some basic data mining techniques such as decision trees, Bayesian learning, linear classification, artificial neural networks, and genetic algorithms, and to learn how to implement these basic algorithms and how to apply them to real-world data mining situations.
(ME&IE 763) Nonlinear Programming (G)
To learn some basic nonlinear programming theory and techniques such as trust-region methods, conjugate gradient methods, quasi-Newton Methods, SQP, genetic algorithm, and simulated annealing, seasoned with demonstrations using MATLAB, and to learn how to implement these basic algorithms and how to apply them to real-world problems.
(ME&IE 772) Linear Statistical Model (G)
To introduce multivariate statistical analysis and the linear statistical models such as linear regression, factor analysis, Bayesian learning, linear classification, and to apply them to business-oriented applications such as marketing.
(ME&IE 861) Continuous-Time Finance (G)
To present arbitrage theory in continuous time using the modern martingale approach and to apply them to interest-rate models.