Location: ERB 129. Time: Tuesdays 7:00pm - 9:40pm
Dr. Chris Ding, 529 ERB Hall. Email: email@example.com
Wed 3:30 - 5:00 (and by appointment).
Wed 3:30-5:00, ERB 204
- Contents and Objectives:
Data mining (DM) is often defined as knowledge discovery in database (KDD).
Today, DM is a broad area of data analysis, exploration, using techniques from
Machine Learning, Artificial Intelligence, Statistics and Database.
This course will cover main topics, including classification, clustering, association rule discovery,
feature selection, dimension reduction, semi-supervised learning.
After completing this course, students will be able
independently analyze data,
finding patterns in it, design and implement practical algorithms
to solve classification problems, such as recognize the hand-written
digits/alphabets, and improve the classification tasks by
using feature selection and dimension reductions.
Undergrad level Linear Algebra
Undergrad level Statistics
Outline and Schedule
Introduction to Data Mining
Pang-Ning Tan, Michael Steinback, Vipin Kumar
- Additional Textbook/reference books:
The following textbooks are for reference purpose.
The mathematics level of the class is approximately same as
Elements of Statistical Learning,
T. Hastie, R. Tibishirani , J. Friedman
2nd edition, Srpinger, 2009
Introduction to Information Retrieval
C. Manning, P. Raghavan, H. Schütze. 2009.
Cambridge University Press, 2009
Course grades will be determined by the following weights:
6 quizzes (required) --- 60%
Class projects (required) --- 20%
Final Exam --- 20%
Any student, found cheating on homeworks, projects, exams,
will get a lower grade below his/her usual grade,
from A to B, from B to C, from C to D, etc.
Class attendence is highly recommanded
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.
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.