Graduate
Classification : Requisite Class Number : 326.513 Credit : 3 Grade : Graduate School
This course deals with probability measurement theory, basics of integration, random variables, independence, various modes of convergence of random variables, random series, law of large numbers, convergence in distribution, characteristic functions and central limit theorems
Classification : Elective Class Number : 326.516 Credit : 3 Grade : Graduate School
This course deals with conditional expectation, martingales, ergodic theorem, infinite divisibility, stabe distribution and general central limit problems.
Classification : Elective Class Number : *326.517A Credit : 3 Grade : Graduate School
This course aims to develop the quality skills of a statistician. Students will improve their abilities to solve statistical problems in various fields of social science as well as those of natural science. Under the professor’s supervision, students are required to present outcomes on real statistical problems and have group discussion.
Classification : Requisite Class Number : 326.519A Credit : 3 Grade : Graduate School
This course deals with theories of statistical estimation including distribution family, sufficiency principle, least square estimator, maximum likelihood estimator, computing skills. Bayesian and minimax aspects of estimators are introduced. Theories related to linear models are covered. Topics include basic linear algebra including the spectral decomposition, distribution theories related to the multivariate normal distribution, projection method, the concept of estimability, Gauss-Markov theory, best linear unbiased estimator(BLUE).
Classification : Requisite Class Number : 326.520A Credit : 3 Grade : Graduate School
This course explores the role of linear models as a statistical tool for modeling data. Theoretical aspects of such models are explored, but the emphasis is on strategies and methodology for model selection, estimation, inference and checking. Models covered include simple and multiple regression, and one-way and two-way analysis of variance for factorial experiments. Inference will be based largely on the least-squares criterion, exploiting the Gauss-Markov theorem, but connections will also be made with likelihood-based approaches. The use of R for modeling data via linear models will be integral to the course.
Classification : Elective Class Number : 326.521 Credit : 3 Grade : Graduate School
This course aims to give you an overview of a number of topics of important current interest in statistics and applied probability, and give you the opportunity to gain an understanding of the practical and theoretical aspects of topics. The subjects we discuss in this course include nonparametric regression, simulation methods, spatial statistics, Classification and robust statistics.
Classification : Elective Class Number : 326.522 Credit : 3 Grade : Graduate School
This course covers statistical inference and asymptotic theories. Uniformly most powerful test, unbiased test, likelihood ratio method are covered. For asymptotic theories, consistency of maximum likelihood estimator and minimum contrast estimator, asymptotic distribution theories of maximum likelihood estimator and its efficiency, delta method are studied. Asymptotic inference methods including likelihood ratio test, Wald test, Rao test, Pearson’s chi-square test are covered.
Classification : Elective Class Number : *326.621A Credit : 3 Grade : Graduate School
This course consists of series of seminars on emerging statistical theory.
Classification : Elective Class Number : 326.626A Credit : 3 Grade : Graduate School
This course introduces theory of nonparametric kernel estimation of density and regression functions. It covers kernel density estimation, local polynomial estimation and the local quasi-likelihood approach for estimating regression functions, nonparametric additive regression function estimation, etc.
Classification : Elective Class Number : *326.631A Credit : 3 Grade : Graduate School
This course consists of series of seminars on emerging applications of statistics.
Classification : Elective Class Number : M1399.001300 Credit : 3 Grade : Graduate School
In this course, we study the theory of Bayesian statistics. In particular, we study constructions of noninformative priors, non-parametric Bayesian statistics, Bayesian asymptotics, and the theory for Bayesian computation.
Classification : Elective Class Number : 326.636 Credit : 3 Grade : Graduate School
This course covers various advanced techniques for analyzing censored and truncated survival data. The topics include Kaplan-Meier estimator, non-parametric maximum likelihood method, empirical likelihood, counting process techniques, martingales and stochastic integration, estimation of hazard function, log-rank and Gehan test, Cox’s proportional hazard model and partial likelihood.
Classification : Elective Class Number : 326.637 Credit : 3 Grade : Graduate School
This course introduces some useful tools and techniques for data mining. Both practical and theoretical sides of data mining are emphasized in this course. For data mining tools, the logistic regression, decision trees and neural networks are covered. For model evaluations, various measures and methods such as lift, score, hit ratio and cross-validation are taught. For advanced tools, ensemble algorithms including bagging and boosting and support vector machine are considered. For unsupervised learning, association rule and clustering methods are considered. Data mining softwares such as R-packages, E-miner, Answer Tree, and Clementine are used to solve practical problems.
Classification : Elective Class Number : 326.638 Credit : 3 Grade : Graduate School
This course will cover statistical methods used to analyze a variety of data in genomics. The course will include a simple overview of genomic data and terminology and will proceed with a review of numerical techniques frequently employed in genomic studies. The course will focus on the statistical methods to cover topics relating to gene expression data analysis and genetic epidemiology such as linkage analysis and tests of association
Classification : Elective Class Number : 326.723A Credit : 3 Grade : Graduate School
This course is the introductory course to regression analysis for master-level graduate students. This course deals with basic matrix algebra and statistical theory, basic regression analysis, inference for simple regression analysis, miscellaneous topics for regression analysis, basic multiple regression analysis, estimation and hypothesis testing, polynomial regression, generalized regression analysis, use of dummy variables, application of analysis of variance, response surface analysis, analysis of mixture experiments, selection of variables, regression diagnostics, biased estimation, nonlinear regression and so on. The prerequisite courses are Statistics and Lab. for basic statistics and linear algebra for matrix theory.
Classification : Elective Class Number : 326.725A Credit : 3 Grade : Graduate School
For the analysis of univariate time series data, ARIMA modeling and spectral theory are introduced. Through real data analysis, students learn the various techniques in the model identification, fitting, diagnostic checking, and forecasting steps.
Classification : Elective Class Number : 326.729A Credit : 3 Grade : Graduate School
This course covers weak convergence of sequence of probability measures on metric spaces, specially on C-space and D-space.
Classification : Elective Class Number : *326.739A Credit : 3 Grade : Graduate School
This course consists of series of seminars on emerging statistical theory and application
Classification : Elective Class Number : *326.746A Credit : 3 Grade : Graduate School
This course covers semigroup theory in Markov process, Hill-Yosida theorem, Brownian motion and the boundary value problem and potential theory.
Classification : Elective Class Number : 326.747 Credit : 3 Grade : Graduate School
This course provides an introduction for using statistical methods to analyze categorical data. Since categorical data can usually be arranged in a contingency table, this course focuses on using statistical methods to analyze contingency tables. The main topics in this course are contingency table analysis, log linear models, and logistic models.
Classification : Elective Class Number : 326.748A Credit : 3 Grade : Graduate School
This course introduces the use of statistical methods to analyze repeated measurement data in experiment data in experimental conditions or at multiple times with one subject. It covers how to use classical multivariate models on multivariate normal distribution and mixed models to analyze continuous repeated measurement data. The course also examines how models based on weighted least squares estimation, random effect models and generalized estimating equations(GEE) are used to analyze repeated measurement data of discrete type.
Classification : Elective Class Number : *326.750A Credit : 3 Grade : Graduate School
In this course, students study recently published papers related to density, regression and frontier function estimation.
Classification : Elective Class Number : M1399.000200 Credit : 3 Grade : Graduate School
Statistical computing becomes ever more important with the advent of Big Data or large scale high-dimensional data. In this course, we study the recent statistical computing techniques for large scale high-dimensional data including statistical computing using GPU and parallel computing.
Classification : Elective Class Number : M1399.000300 Credit : 3 Grade : Graduate School
This course focuses on statistical methods in Spatial Statistics. The goal of the course is to learn the analysis methods for spatial and spatio-temporal data and their theoretical backgrounds and apply such methods. The contents of the course includes but not limited to hypothesis test of spatial dependence, spatial dependence models and estimation, spatial regression and kriging, analysis of areal data, disease mapping, and spatial point process models.
Classification : Elective Class Number : M1399.000400 Credit : 3 Grade : Graduate School
This course builds on Advanced Methods in Data Mining (326.637) focusing on learning deep compositional functions. The goal is to study deep learning methodologies and identify related statistical issues. The contents include pre-deep learning methods such as feature extraction and discrimination; components of well-established machine learning tools (support vector machine, reproducing kernel Hilbert space, model complexity, LASSO, ensemble); neural network; multi-layer-perceptron; backpropagation; convolutional neural network; optimization and regularization; visualization; Python and deep learning frameworks; recurrent neural network; variational inference; generative adversarial network; segmentation; detection; and natural language processing.
Classification : Elective Class Number : M1399.000500 Credit : 3 Grade : Graduate School
Statistical machine learning has been used popularly for data science and artificial intelligence. In this course, various methodologies of statistical machine learning are introduced and their theoretical backgrounds are explained. Supervised learning is mainly considered, and decision theory, high dimensional linear model, nonparametric function estimation, decision tree and ensemble, support vector machine and deep neural networks are included. The empirical risk minimization principle which is a principle applicable to most of those methods are discussed.
Classification : Reading and research Class Number : 326.803 Credit : 3 Grade : Graduate School
Classification : Requisite Class Number : M0000.008700 Credit : 1 Grade : Graduate School
Students are introduced to the faculty and their interests, the field of statistics, computing tips, research ethics and the facilities at the department and the University. Each faculty member gives at least one elementary lecture on some topic of his or her choice. Students are also given information about the e-Learning and Teaching (eTL), the libraries at the University and current bibliographic tools. In addition, students are instructed in the use of the Departmental and University computational facilities and available statistical program packages.
Classification : Requisite Class Number : M0000.008800 Credit : 1 Grade : Graduate School
The statistics department invites experts to Seoul National University to make presentations on the topics of interest in the area of statistics and its applications.
Classification : Requisite Class Number : M1399.000800 Credit : 1 Grade : Graduate School
This seminar course focuses on improving the research, lectures, and related work skills of Ph.D. students in the Department of Statistics. Through lectures, discussions, and seminars from statistics faculty, postdoctoral students, prospective doctoral graduates, and faculty members of the Seoul National University Faculty of Liberal Education, students learn the professional development required for a successful Ph.D. program in statistics. The main contents to be covered during the semester include lecture method seminars to improve lecture skills, statistical research seminars to introduce the latest research topics, and how to write research papers, review reports, research proposals, and resumes.
Classification : Requisite Class Number : M1399.001000 Credit : 3 Grade : Graduate School
This course covers statistical methodologies including data wrangle, data visualization, regression, linear models, generalized linear model, mixed models, and classification, which are essential for master’s level graduate students who are interested in data science. Compared to the traditional courses, this course concentrates on using current systems to do statistics. Less emphasis is made on the underlying theory, which is presented at an intuitive level, and more emphasis is put on using software to implement statistical methods. All statistical analyses of the course are conducted using R and Python.
Classification : Elective Class Number : M1399.001100 Credit : 3 Grade : Graduate School
An Advanced Probabilistic graphical model is a popular framework for encoding joint distributions over complex domains where nodes correspond to variables of interest and the edges of the graph describe conditional dependence information and causal/directional relationships among the variables. Hence, the models are often a foundation of many machine learning approaches for including bioinformatics, social science, image processing, and marketing analysis. This course explains the two basic PGM representations: Bayesian Networks applying a directed graph; and Markov networks applying an undirected graph. The course discusses the theoretical properties of these representations, learning algorithms, as well as their use in practice.
Classification : Elective Class Number : M1399.001200 Credit : 3 Grade : Graduate School
Many questions in empirical scientific research are often causal. This course will introduce statistical theory and methods of causal inference with applications in various scientific fields. Students are expected to understand theoretical frameworks and make valid causal inference. Topics will include potential outcomes; randomized experiments; confounding; observational studies; matching; weighting; propensity score methods; doubly robust; unmeasured confounding bias; sensitivity analysis; instrumental variables; quasi-experimental designs.
