Fall 2018 CSE 4334.001/5334.005

Data Mining

Course Information

Time: Tue/Thu 12:30-1:50pm
Classroom: ERB 130
Homepage: http://crystal.uta.edu/~cli/cse4334 http://crystal.uta.edu/~cli/cse5334

Instructor: Chengkai Li

Office hours: Tue/Thu 2-3pm
Office: ERB 628
Phone: (817) 272-0162
E-mail: cli [AT] uta [DOT] edu
Homepage: http://ranger.uta.edu/~cli

TA: Fatma Arslan

Office hours: Wednesdays 3-5pm
Office: ERB 508
E-mail: fatma [DOT] arslan [AT] uta [DOT] edu

Course Description: This is an introductory course on data mining. Data Mining refers to the process of automatic discovery of patterns and knowledge from large data repositories, including databases, data warehouses, Web, document collections, and data streams. We will study the basic topics of data mining, including data preprocessing, data warehousing and OLAP, data cube, frequent pattern and association rule mining, correlation analysis, classification and prediction, and clustering, as well as advanced topics covering the techniques and applications of data mining in Web, text, big data, social networks, and computational journalism.

Student Learning Outcomes: A solid understanding of the basic concepts, principles, and techniques in data mining; an ability to analyze real-world applications, to model data mining problems, and to assess different solutions; an ability to design, implement, and evaluate data mining software.

Prerequisites:

For CSE 4334: CSE 3330 Database Systems I and IE 3301 Engineering Probability (or MATH 3313 Introduction to Probability) or consent of instructor.
For CSE 5334: prerequisites for CSE5334: There is no official prerequisites. You should have sound CSE background from your Bachelor's program (e.g., programming, data structures and algorithms, discrete mathematics, basics of probabilities and statistics). If you don't have database course from anywhere, you are allowed to take the course, but please get the consent of the instructor. You also must get the consent of the instructor if you have CSE deficiency courses to take.

Textbook

(Required) [TSK] Pang-Ning Tan, Michael Steinbach, and Vipin Kumar. Introduction to Data Mining, Addison-Wesley, 2006. ISBN 0-321-32136-7. (Sample chapters at http://www-users.cs.umn.edu/~kumar/dmbook/index.php)
(Required for relevant chapters) [MRS] Christopher D. Manning, Prabhakar Raghavan and Hinrich Schütze. Introduction to Information Retrieval, Cambridge University Press. 2008. (Free book at http://nlp.stanford.edu/IR-book/)
(Reference) Jure Leskovec, Anand Rajaraman and Jeff Ullman. Mining of Massive Datasets, 2nd ed., Cambridge University Press. (Free book at http://www.mmds.org/#ver21)
(Reference) Jiawei Han, Micheline Kamber and Jian Pei. Data Mining: Concepts and Techniques, 3rd ed. (2nd edition is also fine), Morgan Kaufmann Publishers, June 2011. ISBN 9780123814791.
(Reference) Gareth James, Daniela Witten, Trevor Hastie and Robert Tibshirani. An Introduction to Statistical Learning with Applications in R, 1st ed., Springer, 2013. (Free book at http://www-bcf.usc.edu/~gareth/ISL/index.html)
(Reference) I. H. Witten and E. Frank. Data Mining: Practical Machine Learning Tools and Techniques with Java Implementations, Morgan Kaufmann, 2nd ed. 2005.

Grades

Pop quizzes (best 6 out of 8) 30%
Final Exam 30% (December 11th, Tuesday, 11am-1:30pm. ERB 130)
Programming Assignments (P) 40% (Must be done independently)

The final letter grades will be based on students' performance. There is no pre-defined cutoffs or distribution of grades. Undergraduate and graduate students are compared in separate groups.

Attendance: At The University of Texas at Arlington, taking attendance is not required but attendance is a critical indicator in student success. Each faculty member is free to develop his or her own methods of evaluating students’ academic performance, which includes establishing course-specific policies on attendance. As the instructor of this section, I require all students to attend lectures. However, while UT Arlington does not require instructors to take attendance in their courses, the U.S. Department of Education requires that the University have a mechanism in place to mark when Federal Student Aid recipients “begin attendance in a course.” UT Arlington instructors will report when students begin attendance in a course as part of the final grading process. Specifically, when assigning a student a grade of F, faculty report the last date a student attended their class based on evidence such as a test, participation in a class project or presentation, or an engagement online via Blackboard. This date is reported to the Department of Education for federal financial aid recipients.

Announcements: Stay tuned and make sure to check Blackboard frequently. Important announcements will be posted there.

Assignments and Deadlines

All the assignments must be submitted through Blackboard. We will NOT take hardcopy or email submission, unless the university verifies that Blackboard was malfunctioning or unavailable. If you are not able to submit through Blackboard due to its technical failure, you can email your assignment to us, together with a screenshot showing the technical failure. We will verify with the university.
Everything is due by 11:59pm on the due date. The deadline is automatically managed by Blackboard. You can still turn in assignment after the deadline. However, you automatically lose 5 points per hour after the due time, till you get 0. (Each individual assignment is 100 points.) We cannot waive the penalty, unless there was a case of illness or other substantial impediment beyond your control, with proof in documents.

Regrading: Regrading request must be made within 7 days after we post scores on Blackboard. TA will handle regrade requests. If student is not satisfied with the regarding results, you get 7 days to request again. The instructor will regrade, and the decision is final.

Drop Policy: Students may drop or swap (adding and dropping a class concurrently) classes through self-service in MyMav from the beginning of the registration period through the late registration period. After the late registration period, students must see their academic advisor to drop a class or withdraw. Undeclared students must see an advisor in the University Advising Center. Drops can continue through a point two-thirds of the way through the term or session. It is the student's responsibility to officially withdraw if they do not plan to attend after registering. Students will not be automatically dropped for non-attendance. Repayment of certain types of financial aid administered through the University may be required as the result of dropping classes or withdrawing. For more information, contact the Office of Financial Aid and Scholarships (http://wweb.uta.edu/aao/fao/).

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The Office for Students with Disabilities, (OSD) www.uta.edu/disability or calling 817-272-3364. Information regarding diagnostic criteria and policies for obtaining disability-based academic accommodations can be found at www.uta.edu/disability.
Counseling and Psychological Services, (CAPS) www.uta.edu/caps/ or calling 817-272-3671 is also available to all students to help increase their understanding of personal issues, address mental and behavioral health problems and make positive changes in their lives.

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I pledge, on my honor, to uphold UT Arlington’s tradition of academic integrity, a tradition that values hard work and honest effort in the pursuit of academic excellence.
I promise that I will submit only work that I personally create or contribute to group collaborations, and I will appropriately reference any work from other sources. I will follow the highest standards of integrity and uphold the spirit of the Honor Code.

UT Arlington faculty members may employ the Honor Code in their courses by having students acknowledge the honor code as part of an examination or requiring students to incorporate the honor code into any work submitted. Per UT System Regents’ Rule 50101, §2.2, suspected violations of university’s standards for academic integrity (including the Honor Code) will be referred to the Office of Student Conduct. Violators will be disciplined in accordance with University policy, which may result in the student’s suspension or expulsion from the University. Additional information is available at https://www.uta.edu/conduct/.

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Student Feedback Survey: At the end of each term, students enrolled in face-to-face and online classes categorized as “lecture,” “seminar,” or “laboratory” are directed to complete an online Student Feedback Survey (SFS). Instructions on how to access the SFS for this course will be sent directly to each student through MavMail approximately 10 days before the end of the term. Each student’s feedback via the SFS database is aggregated with that of other students enrolled in the course. Students’ anonymity will be protected to the extent that the law allows. UT Arlington’s effort to solicit, gather, tabulate, and publish student feedback is required by state law and aggregate results are posted online. Data from SFS is also used for faculty and program evaluations. For more information, visit http://www.uta.edu/sfs.

Final Review Week: For semester-long courses, a period of five class days prior to the first day of final examinations in the long sessions shall be designated as Final Review Week. The purpose of this week is to allow students sufficient time to prepare for final examinations. During this week, there shall be no scheduled activities such as required field trips or performances; and no instructor shall assign any themes, research problems or exercises of similar scope that have a completion date during or following this week unless specified in the class syllabus. During Final Review Week, an instructor shall not give any examinations constituting 10% or more of the final grade, except makeup tests and laboratory examinations. In addition, no instructor shall give any portion of the final examination during Final Review Week. During this week, classes are held as scheduled. In addition, instructors are not required to limit content to topics that have been previously covered; they may introduce new concepts as appropriate.

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