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Fall 2020 Applied Analytics PS5440 section 001
FINANCIAL DATA SCIENCE AND MACHINE LEARN
FIN DATA SCI & MACHINE LE

Call Number 22867
Day & Time
Location
M 8:10pm-10:00pm
601 Sherman Fairchild Life Sciences Building
Points 3
Grading Mode Standard
Approvals Required None
Instructor William G Ritter
Type LECTURE
Method of Instruction Hybrid
Course Description This course teaches cutting-edge tools and methods that drive investment decisions at quantitative trading firms, and, more generally, firms applying machine learning to big data. The course will combine presentations of theory, immediately followed by in-class Python programming examples using real financial data. The course will develop a general approach to building models of economic and financial processes, with a focus on statistical learning techniques that scale to large data sets. Among the topics covered are lasso, elastic net, cross validation, Bayesian models, the EM algorithm, Support Vector Machines, kernel methods, Gaussian processes, Hidden Markov Models, and neural networks. The final project will lead the students to build a trading strategy based on the techniques learned throughout the course.
 
Web Site Vergil
Department Applied Analytics
Enrollment 36 students (50 max) as of 8:03AM Thursday, November 26, 2020
Subject Applied Analytics
Number PS5440
Section 001
Division School of Professional Studies
Open To Professional Studies
Campus Morningside
Note MEETS HY-FLEX. APAN ONLY. PRE-REQS: NEEDS ADVISOR APPROVAL
Section key 20203APAN5440K001

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SIS update 11/26/20 08:03    web update 11/27/20 14:18