Fall 2024 Applied Analytics PS5440 section 001

FINANCIAL DATA SCIENCE AND MACHINE LEARN

FIN DATA SCI & MACHINE LE

Call Number 12959
Day & Time
Location
F 2:10pm-4:00pm
417 Mathematics Building
Points 3
Grading Mode Standard
Approvals Required None
Instructor William G Ritter
Type LECTURE
Method of Instruction In-Person
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 45 students (60 max) as of 11:06AM Saturday, December 7, 2024
Subject Applied Analytics
Number PS5440
Section 001
Division School of Professional Studies
Open To Professional Studies
Note ON-CAMPUS. APAN ONLY. PRE-REQS: NEEDS ADVISOR APPROVAL
Section key 20243APAN5440K001