In this course, the instructor will discuss various uses of regression in investment problems, and she will extend the discussion to logistic, Lasso, and Ridge regressions. At the same time, the instructor will introduce various concepts of machine learning. You can consider this course as the first step toward using machine learning methodologies in solving investment problems. The course will cover investment analysis topics, but at the same time, make you practice it using R programming. This course's focus is to train you to use various regression methodologies for investment management that you might need to do in your job every day and make you ready for more advanced topics in machine learning. The course is designed with the assumption that most students already have a little bit of knowledge in financial economics and R programming. Students are expected to have heard about stocks and bonds and balance sheets, earnings, etc., and know the introductory statistics level, such as mean, median, distribution, regression, etc. Students are also expected to know of the instructors' 1st course, 'Fundamental of data-driven investment.' The instructor will explain the detail of R programming. It will be an excellent course for you to improve your programming skills but you must have basic knowledge in R. If you are very good at R programming, it will provide you with an excellent opportunity to practice again with finance and investment examples.

영주닐슨 Nielsen, Youngju 성균관대학교 경영전문대학원 교수 

삼성자산운용 자문위원

주간조선 칼럼니스트

퀀타비움캐피탈 뉴욕 파트너, 최고 투자책임자

•Citi 뉴욕 G10 시스템트레이딩헤드

•JP 모건 뉴욕 채권시스템트레이딩헤드

문의: 성균관대학교 교무처 교육개발센터 (Center for Teaching & Learning)