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Spring 2020 Computer Science W4995 section 013
TOPICS IN COMPUTER SCIENCE
CAUSAL INFERENCE DATA SCIENCE
|Day & Time
834 Seeley W. Mudd Building
|Method of Instruction||Classroom|
|Course Description||Prerequisites: any introductory course in statistics.|
This course introduces fundamental machine learning models, such as logistic regression, decision trees, ensemble algorithms, support vector machine, and clustering analysis, with special emphasis on their learning capacity to solve financial problems. Some of the financial applications explored are algorithmic trading, portfolio optimization, and risk management. This hands-on course, with practical exercises based on Python, is appropriate for students from economics, business, science, and engineering interested in machine learning and finance.
|Enrollment||34 students (42 max) as of 3:39PM Tuesday, March 31, 2020|
|Open To||Architecture, Schools of the Arts, Barnard College, Columbia College, Engineering:Undergraduate, Engineering:Graduate, GSAS, General Studies, SIPA, Journalism|
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