SYST/OR 568. Applied Predictive Analytics

Department of Systems Engineering and Operations Research
George Mason University
Spring 2017

For Syllabus click here

Introduces predictive analytics with applications in engineering, business, finance, health care, and social economic areas. Topics include time series and cross-sectional data processing, data visualization, correlation, linear and multiple regressions, classification and clustering, time series decomposition, factor models and causal models, predictive modeling performance analysis, and case study. Provides a foundation of basic theory and methodology with applied examples to analyze large engineering, social, and econometric data for predictive decision making. Hands-on experiments with R will be emphasized.

Announcements

Course staff

Lecture Notes: Will be made available one-day in advance on Bb
Instructor: Vadim Sokolov
Office: Engineering Building, Room 2242
vsokolov(at)gmu.edu
Tel: 703-993-4533
TA: Bahman Pedrood (bpedrood (at) gmu.edu)

Other Notes

Offie hours

Bahman Pedrood: Wednesday 5-7pm (at Engineering 2241)
Vadim Sokolov: Thursday 5-7pm (at Engineering 2242)

Lectures

Location: Innovation Hall 105
Times: 7:20-10pm on Thursday

Grades

Grade composition: No in-class examination. Grade based entirely on participation in class, homework assigments, take-home midterm and final project.

Textbooks

Links

Deep Learning

Final Project Data

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Notebooks (ISLR labs)

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