Description: An introduction to probability theory and statistics, with an emphasis on solving problems in computer science and engineering. Probability and statistics is an important foundation for computer science fields such as machine learning, artificial intelligence, computer graphics, randomized algorithms, image processing, and scientific simulations. Topics in probability include discrete and continuous random variables, probability distributions, sums and functions of random variables, the law of large numbers, and the central limit theorem. Topics in statistics include sample mean and variance, estimating distributions, correlation, regression, and hypothesis testing. Beyond the fundamentals, this course will also focus on modern computational methods such as simulation and the bootstrap. Students will learn statistical computing using the freely available R statistics software: http://www.r-project.org/.

Class meetings: 3:40 - 5:00pm, Tuesdays and Thursdays in WEB 2230

Instructor: Tom Fletcher
Office: 4686 WEB
Email: fletcher AT cs.utah.edu
Office Hours: Mondays and Wednesdays, 11am - noon

Teaching assistants:
Yang Song
Office: 3421 MEB
Office Hours: Wednesdays 2 - 3pm, Thursdays 10:30 - 11:30am

Namrata Dey
Office: 3409 MEB
Office Hours: Tuesdays 11am - noon

For Help: Email teach-cs3130 AT list.eng.utah.edu to send a question to the instructor and TAs.

Mailing list: Sign up here to receive important class announcements.

Textbook: A Modern Introduction to Probability and Statistics: Understanding Why and How by Dekking, Kraaikamp, Lopuhaa, and Meester.

An electronic version of this book is freely available through the University! The website is here: http://www.springerlink.com/content/978-1-85233-896-1. To access the book you must be visiting this website from the campus network. Or if you are off campus, you can access it using VPN: https://vpnaccess.utah.edu/.

Homework Instructions: Some assignments include both a written and coding part, which are both due by 3:40pm on the due date. Written parts should be turned in physically at the start of class. R source code should be submitted using handin. This can be done by logging into one of the CADE lab Linux machines and running the command:

handin cs3130 hw# hw#.r

(replacing the '#' with the current homework number). There is also a web interface to handin. See this page for more details.