California State University Stanislaus

CS 4950: Big Data

Spring 2015

Meeting days and times will change depending on which professor students are meeting that week

Instructors: Dr. Thomas Carter, Dr. Ayet Hatem, Dr. Melanie Martin, and Dr. Megan Thomas

[Basic Information]        [Announcements]        [Calendar/Assignments]       [Links and Readings]

Welcome to CS 4950, Big Data

Course Description

This course will introduce students to several different aspects of work with computing and "big data." Topics covered may include data mining and machine learning, large graph data analysis, data clustering, and new models for processing large data sets rapidly. Students will work with the instructors in groups on learning and assignments organized around four different themes related to big data, one theme for each instructor. Students will work for one month with one pair of instructors, then for a second month with the other pair of instructors. Students will then select research projects of their own to work on for the final weeks of the semester.

Announcements and Upcoming Events

4/3/2015 Reminder: we (one or more of the professors) would like to receive an email from each student in the class by Wed, 4/15, telling us the project you intend to explore for the next month
Links to pages with interesting data sets have been added to our links page
3/25/2015 Project planning meetings: Thursday, 12:30-2pm, CS conference room, and / or Friday 12 - 1 pm. Please attend both if you can, as there is no way to know ahead of time in which meeting someone will propose a project that appeals to you.
Special presentation: Friday, 1 - 2 pm, DBH 165, about Geographic Information Systems
1/30/2015 For the next four weeks of the semester, student group A will meet with Drs Carter and Hatem on Tuesdays and Thursdays from 1:30 - 3. Student group B will meeting with Drs Martin and Thomas on Wednesdays and Fridays from 12:30 - 2.
All meetings will be in the CS department conference room (next to the CS lab).
1/27/2015 Initial meetings of the class will be held Thurs, 1/29 at 1:15pm and / or Friday, 1/30 at 3pm. Please try to attend one or both of those meetings, and bring your weekly schedule so we can plan future class meetings.
1/27/2015 Welcome to CS 4950!

Basic Information

Recommended Textbook is Data Mining: Practical Machine Learning Tools and Techniques, Third Edition, by Ian H. Witten, Eibe Frank, and Mark A. Hall

Prerequisite: Completion of CS 3100.

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.


Grading and Policies

Final grades will be based on projects and assignments from each instructor, plus a final project. A plus and minus grading scale wll be used to assign final grades. Except for designated collaborative activities in connection with the project, all writing and other work you present for credit must be entirely your own, or developed on your own in consultation with the course instructors. Penalties for representing other people's work as your own will range from No Credit on an assignment through failure of the course and possible University disciplinary action.

Grade Summary:
Assignments and Participation with Dr. Carter 20%
Assignments and Participation with Dr. Hatem 20%
Assignments and Participation with Dr. Martin 20%
Assignments and Participation with Dr. Thomas 20%
Research Project 20%
Total
100%

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.

Attendance: Regular class attendance will be crucial to student learning in this course, and will be expected by all instructors.

University Recording Policy: Audio or video recording (or any other form of recording) of classes is not permitted unless expressly allowed by the faculty member as indicated in the course syllabus or as a special accommodation for students who are currently registered with the Disability Resource Services Program and are approved for this accommodation. Recordings allowed as special accommodations are for the personal use of the DRS-approved student, and may only be distributed to other persons who have been approved by the DRS program. Faculty may require the student sign an Audio / Video Recording Agreement, which they may keep for their records.

University Disability Services: CSU Stanislaus respects all forms of diversity. By university commitment and by law, students with disabilities are entitled to participate in academic activities and to be tested in a manner that accurately assesses their knowledge and skills. They also may qualify for reasonable accommodations that ensure equal access to lectures, labs, films, and other class-related activities.   Please see the instructor if you need accommodations for a registered disability.  Students can contact the Disability Resource Services office for additional information.  The Disability Resource Services website can be accessed at http://www.csustan.edu/DRS/
Phone: (209) 667-3159