CS 6957: Probabilistic Modeling
Spring 2013
Class meetings: 2:00 - 3:20pm, Tuesdays and Thursdays in WEB 1250
Instructor: Tom Fletcher
Office: 4686 WEB
Email: fletcher AT cs.utah.edu
Office Hours: After class, Wed. 11:00am - noon, or by appointment.
TA: Evan Young
Office: MEB 3115
Email: evan.d.young AT gmail.com
Office Hours: Wed. 3:00 - 5:00 pm, Thu. 11:30am - 1:30pm
Mailing list: Sign up here to receive important class announcements.
Description: This course focuses on how to use probability theory to model and analyze data. Data in the real world almost always involves uncertainty. This uncertainty may come from noise in the measurements, missing information, or from the fact that we only have a randomly sampled subset from a larger population. Probabilistic models are an effective approach for understanding such data, by incorporating our assumptions and prior knowledge of the world. These ideas are important in many areas of computer science, including machine learning, data mining, natural language processing, computer vision, and image analysis.
Prerequisites: Students should have a basic knowledge of probability. This is a graduate course, but undergraduates who excelled in CS 3130 are invited to attend with instructor permission (please send me email if you are interested). Those interested in brushing up on their probability might take a look at the material from last semester's CS 3130. See the online textbook, lecture notes, and homeworks.
Textbook: There is no official textbook for the course. Here are a couple of books that are available online through the University of Utah Library. We will cover some of the chapters in these books:
Jim Albert, Bayesian Computation in R
Mary Kathryn Cowles, Applied Bayesian Statistics
To access the books you must be visiting these websites from the campus network. Or if you are off campus, you can access it using VPN: https://vpnaccess.utah.edu/.
Software: All programming will be in R, a powerful, open-source statistical software package and programming language: http://www.r-project.org No knowledge of R is expected coming in.
Assignment Submission: Submit all assignments electronically through the Canvas page for this course. Written assignments must be PDF (formatted in LaTeX) and R code in a plain ASCII *.r file.
Instructor: Tom Fletcher
Office: 4686 WEB
Email: fletcher AT cs.utah.edu
Office Hours: After class, Wed. 11:00am - noon, or by appointment.
TA: Evan Young
Office: MEB 3115
Email: evan.d.young AT gmail.com
Office Hours: Wed. 3:00 - 5:00 pm, Thu. 11:30am - 1:30pm
Mailing list: Sign up here to receive important class announcements.
Description: This course focuses on how to use probability theory to model and analyze data. Data in the real world almost always involves uncertainty. This uncertainty may come from noise in the measurements, missing information, or from the fact that we only have a randomly sampled subset from a larger population. Probabilistic models are an effective approach for understanding such data, by incorporating our assumptions and prior knowledge of the world. These ideas are important in many areas of computer science, including machine learning, data mining, natural language processing, computer vision, and image analysis.
Prerequisites: Students should have a basic knowledge of probability. This is a graduate course, but undergraduates who excelled in CS 3130 are invited to attend with instructor permission (please send me email if you are interested). Those interested in brushing up on their probability might take a look at the material from last semester's CS 3130. See the online textbook, lecture notes, and homeworks.
Textbook: There is no official textbook for the course. Here are a couple of books that are available online through the University of Utah Library. We will cover some of the chapters in these books:
Jim Albert, Bayesian Computation in R
Mary Kathryn Cowles, Applied Bayesian Statistics
To access the books you must be visiting these websites from the campus network. Or if you are off campus, you can access it using VPN: https://vpnaccess.utah.edu/.
Software: All programming will be in R, a powerful, open-source statistical software package and programming language: http://www.r-project.org No knowledge of R is expected coming in.
Assignment Submission: Submit all assignments electronically through the Canvas page for this course. Written assignments must be PDF (formatted in LaTeX) and R code in a plain ASCII *.r file.