SYST/OR 468. Applied Predictive Analytics

Department of Systems Engineering and Operations Research
George Mason University
Spring 2019

For course materials click here

SYST 468 is an undergraduate course focused on applying statistical and machine learning methodologies to develop predictive models. We will learn both classical methods for regression and classification, such as linear regression and logistic regression as well as new methods such as deep learning. We will consider applications in engineering, finance and artificial intelligence. There will be an emphasis on assumptions and interpretation. Although basics of probability and statistics will be revisited, it is targeted towards students who have completed (and remember the concepts from) a course in introductory statistics. We will make extensive use of computational tools, such as the R language for statistical computing, both for illustration in class and in homework problems.


  • 04/08/2019: HW3 posted, due date April 17.
  • 03/09/2019: HW2 posted, due date April 3.
  • 02/24/2019: Midterm is on March 20 (open book)
  • 02/24/2019: No class on March 13 (Spring Break)
  • 02/24/2019: H1 Due date is March 6
  • 10/10/2018: Check back regularly for announcements
  • 10/10/2018: First Class is on January 23

Course staff

Lecture Notes: Will be made available one-day in advance on Bb
Instructor: Vadim Sokolov
Office: Eng Building, Room 2242
Tel: 703-993-4533

Offie hours

Vadim Sokolov: Wed 2:30-4:30pm (at Engineering 2242)
TA: Jungho Park: Mon 1-3pm (at Egnineering 2216)


Location: Planetary Hall 206
Times: 4:30-7:10pm on Wednesday


Grade composition: Grade based on participation in class, in-class midterm, homework assignments, and final project.


  • Diez, Barr and Cetinkaya-Rundel Statistics, OpenIntro, 2015
  • James, Witten, Hastie and Tibshirani, R, Springer, 2009.
  • Additional reading: List

Deep Learning

Previous instances