[Basic Information] [Announcements] [Calendar/Assignments] [Links and Readings]
This course will introduce students to several different aspects
of work with computing, "big data" and cyber-security. Topics covered may
include data mining and machine learning, data clustering, new
models for processing large data sets rapidly, database and app security.
Students will work with the instructors
in groups on learning and assignments organized around three different themes
related to big data or cyber-security, one theme for each instructor.
Students will then select research projects of their own to
work on for the final weeks of the semester.
Announcements and Upcoming Events
|8/25/2017||Student groups will meet Wednesdays, at 2pm or 4pm. See schedule for more details.|
|8/24/2017||All students doing research need to complete the Responsible Conduct of Research Certificate, and bring a copy of the certificate to Dr Martin, Dr Thomas or Dr Carter. (Or email a .pdf of the certificate to Dr Martin.)|
|8/23/2017||Sign up here for defensive driving. Get a certificate when you finish it, and give a copy of the certificate to Marlys Knutsen in the CS office.|
|8/23/2017||Sign up for MentorNet!|
|8/23/2017||Welcome to CS 4950!|
Required - access to the CSU Stanislaus Library's Safari On-line ebook collection.
Recommended Textbook is Data Mining: Practical Machine Learning Tools and Techniques, Third Edition, by Ian H. Witten, Eibe Frank, and Mark A. Hall (Fourth edition available in Safari On-Line as of August 2017)
Prerequisite: Consent of instructor.
Warning: We reserve the right to make changes to the
syllabus at any time during the term by announcing them via email
and on this web page.
|Assignments and Participation with Dr. Carter||25%|
|Assignments and Participation with Dr. Martin||25%|
|Assignments and Participation with Dr. Thomas||25%|
Academic Honesty: The work you do for this course will be
your own, unless otherwise specified. You are not to submit other
people's work and represent it as your own. We consider academic
honesty to be at the core of the University's activities in
education and research. Academic honesty is expected at all times
in this course.