Instructor of CIS 4930/CAP 5771: Data Mining - 2024 Fall

Administrivia

• 📢 Instructor: Dr. Yushun Dong (yd24f[at]fsu[dot]edu)
• 💡 Teaching Assistants: Md Shamim Seraj (mseraj[at]fsu[dot]edu)
• 📅 Time: Mondays & Wednesdays, 4:50 pm-6:05 pm (ET)
• 🏫 Location: Classroom Building (HCB) Profile 216
• 🔍 Instructor Office Hours: Monday and Wednesday (6:05 to 7:05 PM at HCB 216).
• 🔍 TA Office Hours: Tuesdays and Thursdays (2:00 to 4:00 PM at Love 359).
• 🎒 Format: In-person only (unless there is a drastic change in the situation).

Course Overview

🚀 Welcome to the fascinating field of data mining, a discipline at the intersection of computer science, statistics, and business intelligence! Throughout this course, we’ll explore various data mining techniques, from classification and clustering to association analysis and anomaly detection. You’ll learn how to prepare data, select appropriate algorithms, and interpret results. Real-world examples and case studies will illustrate the practical applications of data mining across diverse industries.

📘 This course will draw materials from the textbook and data mining and machine learning literature. You will study the materials, complete written assignments, work on a course project, present your project in class, and take a final exam. You are expected to have a working knowledge of basic probability theory and linear algebra. In addition, good programming skills are required for the course project.

Textbook

Introduction to Data Mining

Authors: Pang-Ning Tan, Michael Steinbach and Vipin Kumar

Website: https://www-users.cs.umn.edu/~kumar001/dmbook/index.php

Prerequisite

No hard prerequisite.

Recommended prerequisite: ISC 3222 or ISC 3313 or ISC 4304C or COP 3330 or COP 4530.

If you have not taken any of the prerequisite above, you are recommended to complete one Kaggle competition (a most famous and simple example is here) — this will bring you a sense of how the project and homework of this course would be like and what knowledge we are going to learn. Take this course if you like them :)

Grading

• Assignments (10%): There will be several homework assignments (written and coding-based) spaced out over the course of the semester. All the assignments will be equally weighted. Submission and other instructions will be posted on Canvas.

• Project Proposal (20%): There will be a semester-long project where the goal is to solve a challenging real-world data mining problem. Students will work in groups for this term project. Students will need to submit a project proposal outlining the project idea with a hard deadline of 23:59 PM (ET) on October 4th, 2024. This project proposal is strictly two-page maximum for the main content, with unlimited pages of references and appendices, together with any type of supplementary materials under 50 MB.

• Final Project Report & Presentation (50%): Students will need to submit a final report (20% of final grades) and the code with a hard deadline of 23:59 PM (ET) on December 6th, 2024. This project report is strictly eight-page maximum for the main content, with unlimited pages of references and appendices, together with any type of supplementary materials under 50 MB. Only Python or MATLAB will be allowed for the implementations. Students will also be required to present their projects (30% of final grades) at the end of this semester: 11.20th, 11.25th, 12.2nd, and 12.4th (subject to changes depending on the final number of groups). Six slots will be assigned for each class in a random order among all groups. Each group will be given 10 minutes for presentation and 2 minutes for Q&A (12 minutes in total).

Please see a detailed introduction of Project Proposal and Final Project Report & Presentation here.

• Final Exam (20%): There will be a final exam at the end of the semester (Monday, Dec 9, 2024; 5:30 p.m. – 7:30 p.m. at HCB 216; we will not have class for the whole week). All questions will be closely related to the lecture content. An A4-size one-page cheat sheet will be allowed.

• 🎁 Extra Bonus: (1) Students are highly encouraged to prepare for submissions to major AI/ML/DM conferences based on their projects. Please be sure to make an appointment with the instructor prior to any submission plans to perform a comprehensive evaluation of the research topic. Each submission under the instructor's recognition will gain 7 points on their final grades; (2) Students are highly encouraged to provide feedback on the development of this course. At the end of this semester, a feedback survey completion rate exceeding 70% leads to an additional 7% for everyone’s actual grade, i.e., your_final_grade = your_actual_grade * 107%.

Schedule

Course Policies

Missed Exam Policy: Unexcused missed exams and homework will be given a grade of 0. See the University Attendance Policy for a discussion of valid reasons to excuse absences (https://registrar.fsu.edu/bulletin/graduate/information/academic_regulations/).

Grade of “I” Policy: Incomplete (“I”) grades should be recorded only in exceptional cases when a student, who has completed a substantial portion of the course and who is otherwise passing, is unable to complete a well-defined portion of a course for reasons beyond the student’s control. Students in these circumstances must petition the instructor and should be prepared to present documentation that substantiates their case.

University Attendance Policy: Excused absences include documented illness, deaths in the family and other documented crises, call to active military duty or jury duty, religious holidays, and official University activities. These absences will be accommodated in a way that does not arbitrarily penalize students who have a valid excuse. Consideration will also be given to students whose dependent children experience serious illness.

Academic Honor Policy: The Florida State University, Academic Honor Policy, outlines the University’s expectations for the integrity of student’s academic work, the procedures for resolving alleged violations of those expectations, and the rights and responsibilities of students and faculty members throughout the process. Students are responsible for reading the Academic Honor Policy and for living up to their pledge to . . . be honest and truthful and . . . [to] strive for personal and institutional integrity at Florida State University. (Florida State University Academic Honor Policy, found at http://fda.fsu.edu/Academics/Academic-Honor-Policy).

For this course, in particular, every student must complete his/her assignments, quizzes, and exams independently. Showing your work to your peers or making it accessible to them is considered academic dishonesty. You are responsible for ensuring that your work is adequately protected and not accessible to others.

Americans with Disabilities Act: Students with disabilities needing academic accommodation should: (1) register with and provide documentation to the Office of Accessibility Services; (2) bring a letter to the instructor indicating the need for accommodation and what type; (3) meet (in person, via phone, email, skype, zoom, etc…) with each instructor to whom a letter of accommodation was sent to review approved accommodations. Please note that instructors are not allowed to provide classroom accommodation to a student until appropriate verification from the Office of Accessibility Services has been provided. This syllabus and other class materials are available in an alternative format upon request. For more information about services available to FSU students with disabilities, contact the: Office of Accessibility Services, 874 Traditions Way, 108 Student Services Building, Florida State University, Tallahassee, FL 32306-4167; (850) 644-9566 (voice); (850) 644-8504 (TDD), oas@fsu.edu, https://dsst.fsu.edu/oas/

Confidential Campus Resources: Various centers and programs are available to assist students with navigating stressors that might impact academic success. These include the following:

• Victim Advocate Program University Center A, Room 4100, (850) 644-7161, Available 24/7/365, Office Hours: M-F 8-5 https://dsst.fsu.edu/vap

• University Counseling Center, Askew Student Life Center, 2nd Floor, 942 Learning Way. (850) 644-8255 https://counseling.fsu.edu/

• University Health Services Health and Wellness Center, (850) 644-6230 https://uhs.fsu.edu/

Free Tutoring from FSU: On-campus tutoring and writing assistance is available for many courses at Florida State University. For more information, visit the Academic Center for Excellence (ACE) Tutoring Services’ comprehensive list of on-campus tutoring options at http://ace.fsu.edu/tutoring or contact tutor@fsu.edu. High-quality tutoring is available by appointment and on a walk-in basis. These services are offered by tutors trained to encourage the highest level of individual academic success while upholding personal academic integrity.

Late Policy and Make-up Exams:

• Late assignments will not ordinarily be accepted. If, for some compelling reason, you cannot hand in an assignment on time, please contact the instructor as far in advance as possible.
• No credit will be given to late course projects.
• No make-up exams (except under extremely unusual circumstances).

Syllabus Change Policy: Except for changes that substantially affect the implementation of the evaluation (grading) statement, this syllabus is a guide for the course and is subject to change with advance notice.