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

**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**: TBA

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

**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 OpenIntro Statistics, OpenIntro, 2015

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

Additional reading: Data Science Reading List

Airbnb (Random Forest)

Facebook (Decision trees and logistic regrsssion)

Youtube (deep learning)

Uber (time series)