**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.

01/11/2018: Payed summer internship in analytics @ GMU: details. Apply by Feb 15!

12/17/2017: Check back regularly for announcements

**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**: TBD

Vadim Sokolov: Wed 2:30-4:30pm (at Engineering 2242)

**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.

Diez, Barr and Cetinkaya-Rundel OpenIntro Statistics, OpenIntro, 2015

James, Witten, Hastie and Tibshirani, An Introduction to Statistical Learning with Applications in R, Springer, 2009.

Hyndman and Athanasopoulos, Forecasting: Principles and Practice, OTexts, 2013.

Additional reading: Data Science Reading List

Airbnb (Random Forest)

Facebook (Decision trees and logistic regrsssion)

Youtube (deep learning)

Uber (time series)