CIS4930: Data Mining (Spring 2017)

Instructor: Peixiang Zhao

| Syllabus | Announcement | Schedule | Assignment | Resources |



Course Description

Data in the information era is accumulating at an incredible rate due to a host of technological advances. Electronic data capture has become inexpensive and ubiquitous as a by-product of innovations such as the Internet, e-commerce, point-of-sale devices, bar-code readers, and intelligent machines. Such data is often stored in databases or on the Web specifically intended for decision support and business intelligence. Data mining is a rapidly growing field that is concerned with developing techniques and algorithms to make intelligent use of these big data repositories. A number of successful applications have been reported in a wide range of areas such as credit rating, fraud detection, database marketing, customer relationship management, and social network analysis.

As an introductory course on data mining, this course introduces the key concepts, principles, algorithms, and systems of data mining, including, but not limited to (1) what is data mining? (2) get to know your data, (3) data preprocessing, integration and transformation, (4) mining frequent patterns and assoications, (5) classification, and (6) cluster analysis. The course will primarily serve undergraduate students interested in the fields of data mining and knowledge discovery. Also, the course may attract students from other disciplines who need to understand, develop, and use data mining techniques and systems to analyze large amounts of data.

Basic Information

Administrivia

Textbook

Reference

Prerequisites

COP3330: Object-oriented Programming and COP4530: Data Structures and Algorithms or equivalents courses are required. Students should come with good programming skills and basic knowledge in probability and linear algebra. If you are not sure whether you have the right background, please contact the instructor.

Note: Students need to be familar with at least one programming language, such as C/C++, Java, or Python. We will not cover programming-specific issues in this course.

Format and Activities

This course will draw materials from the textbook as well as data mining and machine learning literature. Students will study the materials, do both programming and written assignments, take a series of in-class quizzes, a midterm exam, and a final exam.

  • Lectures and reading: we encourage (and appreciate!) students to attend classes, because effective lectures rely on students' participation to raise questions and contribute in discussions. We will provide lecture notes and related readings before class, which will be posted on the schedule page.

    Read the textbook for the required reading before lectures, and study them more carefully after class. Please note that all the required readings are fair materials for exams. These materials may not be fully covered in lectures. Our lectures are intended to motivate as well as provide a road map for your reading-- with the limited lecture time we may not be able to cover everything in the readings.

  • Questions: We encourage students discussing their questions and problems first with peers and classmates. This way, you can get immediate help and also learn to communicated "professionally" with your classmates. In any case for more thorough discussion, come to the office hours of TA's and the instructor's. Any announcement will be posted on the announcement page. Make sure to check it frequently enough to stay informed.

  • Assignments: There will be four homework including both written assignments and programming problems spaced out over the course of the semester. All the assignments should be done individually by the students. Assignments should be submitted before the class begins on the due dates.

  • Quizzes: There will be a series of in-class quizzes with an aim of testing basic understanding of key concepts and knowledge, and calling for attendence in classes.

  • Exam: There will be an in-class midterm exam held in the middle of the semester, and a final exam at the end of the semester.

    Course Policies

    General Policy

    Collaboration/Academic Honesty

    All course participants must adhere to the academic honor code of FSU which is available in the student handbook. All instances of academic dishonesty will be reported to the university. Evey student must write his/her own homework/code (unless you are in the same group for the programming progject). Showing your code or homework solutions to others is a violation of academic honesty. It is your responsibility to ensure that others cannot access your code or homework solutions. Consulting related textbooks, papers and information available on Internet for your assignment and homework is fine. However, copying a large portion of such information will be considered as academic dishonesty. If you borrow a small piece of any such information, please acknowledge that in your assignment. Please see the following web site for a complete explanation of the Academic Honor Code.

    Late Policy and Make-up Exams

    Students with Disabilities

    Americans With Disabilities Act: Students with disabilities needing academic accommodation should: (1) register with and provide documentation to the Student Disability Resource Center; (2) bring a letter to the instructor indicating the need for accommodation and what type. This syllabus and other class materials are available in alternative format upon request. For more information about services available to FSU students with disabilities, contact the: Student Disability Resource Center: 874 Traditions Way, 108 Student Services Building, Florida State University, Tallahassee, FL 32306-4167. (850) 644-9566 (voice), (850) 644-8504 (TDD), sdrc@admin.fsu.edu, http://www.disabilitycenter.fsu.edu/.

    Grading Policy

    The course grade will break down as follows,

    Any regrading request should be submitted to the intructor or the TA(s) within one week since the graded deliverables are handed out to students.

    Your final grade will be assigned as follows,

    This table indicates minimum guaranteed grades. Under certain limited circumstances (e.g., an unreasonably hard exam), we may select more generous ranges or scale the scores to adjust.

    Last updated: Dec.24th, 2016