SYST 468. Applied Predictive Analytics

Department of Systems Engineering and Operations Research
George Mason University
Spring 2018
For Syllabus 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.


Course staff

Lecture Notes: Will be made available one-day in advance on Bb
Instructor: Vadim Sokolov
Office: Engineering Building, Room 2242
Tel: 703-993-4533
TA: Jungho Park (jpark98@gmu.edy)

Offie hours

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


Location: Planetary Hall 127
Times: 4:30-7:10pm on Wednesday


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



Deep Learning