Location: TBD. Time: TBD
Instructor:
Dr. Chris Ding, ERB 529. Phone: 817-272-7041.
Email: chqding@uta.edu
Office Hours:
- Contents and Objectives:
-
In Spring 2012, topics will cover advances data mining
(see
Course Flyer
for Spring 2010), sparse and low-rank machine learning, text mining,
matrix and tensor completion (recommender system).
This course teaches both hands-on skills and
in-depth theory in data mining.
- Prerequisites:
-
Must have taken at least one of the
following 4 courses: CSE4334/5334:Data Mining, CSE6363:Machine Learning,
CSE5367:Pattern Recognition,
CSE4309/5361:Artificial Intelligence II.
Addtionally,
CSE4345/5435: Computational Mehods,
CSE 5301:Data Modeling/Statistics,
and CSE5311:computer algorithms are highly recommended.
This advanced course requires good knowledge of mathematics and graphs.
- Textbook & References:
-
1. Introduction to Data Mining, Vipin Kumar, 2007
2. Pattern Classification, R.O. Duda, P.E. Hart, D.G. Stork, 2002
3. Pattern Recognition and Machine Learning, Chris Bishop, 2008
4. Tutorials by Chris Ding.
- Grades
-
-
Homework --- 20%
-
One Exam (2 weeks before end of semester)--- 30%
-
Three class projects and presentation (2-person teams) --- 50%
Attendance
Attendance is mandatory,
Americans With Disabilities Act
The University of Texas at Arlington is on record as being committed to both the spirit and letter of federal equal opportunity legislation; reference Public Law 93112 -- The Rehabilitation Act of 1973 as amended. With the passage of new federal legislation entitled Americans With Disabilities Act - (ADA), pursuant to section 504 of The Rehabilitation Act, there is renewed focus on providing this population with the same opportunities enjoyed by all citizens.
As a faculty member, I am required by law to provide "reasonable accommodation" to students with disabilities, so as not to discriminate on the basis of that disability. Student responsibility primarily rests with informing faculty at the beginning of the semester and in providing authorized documentation through designated administrative channels.
Academic Dishonesty
It is the philosophy of The University of Texas at Arlington that academic dishonesty is a completely unacceptable mode of conduct and will not be tolerated in any form. All persons involved in academic dishonesty will be disciplined in accordance with University regulations and procedures. Discipline may include suspension or expulsion from the University.
"Scholastic dishonesty includes but is not limited to cheating, plagiarism, collusion, the submission for credit of any work or materials that are attributable in whole or in part to another person, taking an examination for another person, any act designed to give unfair advantage to a student or the attempt to commit such acts." (Regents’ Rules and Regulations, Part One, Chapter VI, Section 3, Subsection 3.2, Subdivision 3.22)
Student Support Services Available
The University of Texas at Arlington supports a variety of student success programs to help you connect with the University and achieve academic success. These programs include learning assistance, developmental education, advising and mentoring, admission and transition, and federally funded programs. Students requiring assistance academically, personally, or socially should contact the Office of Student Success Programs at 817-272-6107 for more information and appropriate referrals.
2001-08-20