KAIStat

Hi I am planning to apply for a number of Statistics MS program at the end of this year. Now i am considering what courses to take in these upcoming Spring semester(the last semester that can be included in my transcript for 17F application ) and I need some advice on it. Undergrad: One of Top 5 Univ. in QS Asia University ranking.GPA: 3.7/4.00Major: Industrial Engineering / Math. Related courses i took : Calculus1(B) Multivariable calculus(A) Differential equations(B-) Probability and statistics(A) Engineering statistics1(B) Engineering statistics2(A+) Mathematical statistics(A-) Operations Research(A-) Stochastic Process (A+) Real analysis(B+) Regression and Experiement design(B+) Data structures(A) abstract Linear algebra(A-) RA/TA experience: None. Half an year of undergraduate research program the first author on one working papers in one of the not well known Journal in Management science I should choose two among following three courses : 1. graduate level stat course: Statistical inference book by Casella berger (Exponential families, Hierarchical models, Principles of data reduction, Estimation theory, Bayesian decision problems, Evaluation of estimators, Regression models. ) : I'm considering to take this course hoping this course on my transcript would show my strong mathematical background, but I'm not sure whether it really would. 2. Measure theory based probability theory : I'm taking this course to compensate my defect in real analysis grade(B+). If it is not that needed, I really don't want to take this since it is a bit hard to get A here.. 3. graduate level Statistical learning course(=machine learning) : I think maybe this course on my transcript would show my ability to do some programming. Which option seems better? I wonder whether the committee closely looks at the advanced stat course or the more analysis-based probability theory course or machine learning course. Thank you so much