1. Statistics
| Course | Grade | Content | Textbook |
|---|---|---|---|
| Introduction to Statistics | A+ | definition of probability, discrete and continuous random variable, sampling, estimation, hypothesis test, inference between two populations | Korean textbook |
| Statistical Methods | A+ | One-way ANOVA, randomized complete block design, two-way ANOVA, chi-squared test, goodness-of-fit test, simple and multiple linear regression | instructor-prepared materials |
| R and Python Programming | A+ | basic concepts of Python and R | instructor-prepared materials |
| Mathematical Statistics (1) (undergraduate course) | A+ | probability set function, special expectations, multivariate dstribution, transformation of random variables, discrete random variables, continuous random variables, order statistics, consistency, delta-method, MGF, CLT | Hogg, Introduction to Mathematical Statistics, 8th ed. |
| Mathematical Statistics II | A+ | MLE, Rao–Cramér lower bound, maximum likelihood tests, MVUE, sufficient statistic, Lehmann–Scheffé theorem, exponential class, Neyman–Pearson theorem, UMP tests | Hogg, Introduction to Mathematical Statistics, 8th ed. |
| Regression Analysis | A+ | inference and prediction in regression analysis, Bonferroni joint confidence intervals, Gauss-Markov theorem, model selection and validation, Cook’s distance, variance inflation factor, weighted least squares | Kutner, Applied Linear Regression Models, 5th ed. |
| Sampling Theory in Data Science | A+ | simple random sampling, stratified random sampling, ratio estimation, regression estimation, cluster sampling, sampling with unequal probability, model-based approach | Lohr, Sampling: Design and Analysis, 2nd ed. |
| Deep Learning | A | neural network, activation function, forward and back propagation, supervised and unsupervised learning, ROC curve, CNN, RNN, reinforcement learning, Bellman equation | instructor-prepared materials |
| Statistical Computing | A+ | core theory and algorithms in statistical computing: inverse transform method, rejection sampling, bootstrapping, variance reduction techniques, MCMC, Gibbs sampling, Newton–Raphson method | Ross, Simulation, 5th ed. |
| Time Series Analysis | A+ | moving average method, exponential smoothing, decomposition, AR model, MA model, ARMA model, ARIMA model, SARIMA, transfer function, cross-correlation function, ARCH and GARCH model, VAR model | instructor-prepared materials |
| Multivariate Analysis | A+ | principal component analysis, factor analysis, maximum likelihood method, principal component method, cluster analysis, Fisher’s LDA. | Wolfgang, Applied Multivariate Statistical Analysis, 4th ed. |
| Linear Model | A+ | multivariate distributions, distribution of quadratic forms, estimability, generalized least squares estimator, nested hypotheses, ANOVA, contrast | instructor-prepared materials |
| Bayesian Statistics | A+ | posterior estimate, posterior predictive p-value, choice of prior distribution, implicit meaning of Bayesian analysis, Laplace approximation, variatonal linear regression, composition method | Gelman, Bayesian Data Analysis, 3rd ed. |
| Statistical Learning Theory | A+ | canonical correlation analysis, Gauss Markov theorem, subset selection, linear methods for Classi cation, bases expansions, reproducing kernel Hilbert space, model assessment. | Hastie, The Elements of Statistical Learning, 2nd ed. |
| Statistical Theory for High-dimensional and Big Data | A+ | Hoeffding's inequality, Bernstein's inequality, metric entropy, covariance estimation in the operator norm, sparse PCA, Rademacher complexity | instructor-prepared materials |
| Mathematical Statistics 1 (graduate course) | A+ | advanced topics in mathematical statistics: Gumbel–Max trick, semi-supervised mean estimation, Efron–Stein inequality, Hoeffding’s inequality, median-of-means estimator, minimax estimation, Stein’s paradox | Casella & Berger, Statistical Inference, 2nd ed. |
| Advanced Bayesian Methods | A+ | application to linear mixed model and GLM, Bayesian nonparametric regression, Goussian process, finite mixture models, Dirichlet process | Gelman, Bayesian Data Analysis, 3rd ed. |
| Nonparametric Function Estimation | A+ | kernel density estimator, bandwidth selection, local polynomial regression, B-spline, smoothing spline, reproducing kernel Hilbert spaces, smoothing spline ANOVA | Wand, Kernel Smoothing & Chong Gu, Smoothing Spline ANOVA Models, 2nd ed. |
| Monte Carlo Methods | A+ | advanced MCMC and Monte Carlo methods: adaptive rejection sampling, Rao–Blackwellization, Pólya–Gamma augmentation, reversible jump MCMC, Hamiltonian Monte Carlo, approximate Bayesian computation, slice sampling, parallel tempering | instructor-prepared materials |
| Generalized Mixed Models | A+ | hierarchical model, random effects, generalized linear models, linear mixed models, application to missing data, repeated measures ANOVA, generalized linear mixed model, repeated measures within repeated measures, generalized estimating equations | McCulloch & Searle Generalized, Linear, and Mixed Models |
2. Mathematics
| Course | Grade | Content | Textbook |
|---|---|---|---|
| Engineering Mathematics(1) | B+ | chain rule, mean value theorem, L'Hospital's rule, techniques of integration, polar coordinates, sequence and series | Stewart, Calculus: Early Transcendentals, 6th ed. |
| Engineering Mathematics(2) | A+ | vector functions, partial derivatives, multiple integrals over regions and polar coordinates, Green's theorem, Stokes' theorem | Stewart, Calculus: Early Transcendentals, 6th ed. |
| Engineering Mathematics(3) | A+ | first-order ODE, second order ODE, nonhomogeneous ODE, systems of ODEs, Legendre’s equation, Frobenius method, Bessel’s equation, Laplace transform, convolution | Kreyszig, Advanced Engineering Mathematics, 10th ed. |
| Engineering Mathematics(4) | A+ | gradient, divergence, curl of vector field, Fourier series, Fourier transform, heat equation, complex numbers, Cauchy–Riemann equations, trigonometric and hyperbolic functions | Kreyszig, Advanced Engineering Mathematics, 10th ed. |
| Probability and Random Variable | A+ | An introductory probability course for undergraduate EE students, covering up to the scope of Mathematical Statistics(1) (excluding hypothesis testing). | Yates & Goodman, Probability and Stochastic Processes, 3rd ed. |
| Linear Algebra and Its Application | A+ | engineering students only, solution-focused: system of linear equations, matrix algebra, kernal and range, LU and QR decomposition, dimension theorem, diagonalization | Anton, Contemporary Linear Algebra |
| Analysis (1) | A | basic topology, sequences and series, uniform continuity, differentiation, Riemann–Stieltjes integral, uniform convergence. | Rudin, Principles of Mathematical Analysis, 3rd ed. |
| Linear Algebra | A+ | An Applied Statistics Department course covering both theory (proofs) and computation; it shares the same topics as the engineering course. | Anton, Contemporary Linear Algebra |
| Real Analysis 1 | A+ | Lebesgue measure, measurable functions, Lebesgue integral, Fubini’s theorem, Lebesgue differentiation theorem, Hilbert spaces, Fatou’s theorem. | Stein, Real Analysis: Measure theory, Integration, and Hilbert spaces. |
3. GPA
(1) Undergraduate
Cumulative GPA : 4.02/4.3 (3.85/4.0 using 0.3-step, 3.86/4.0 using 0.33-step)
Last Two Years GPA: 4.21/4.3 (4.0/4.0)
(2) Master
Cumulative GPA : 4.3/4.3 (4.0/4.0)