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COMP5331: Knowledge Discovery and Data Mining

COMP5331: Knowledge Discovery and Data Mining. Instructor: Lei Chen. 1. Course Details. Course URL: http://www.cse.ust.hk/~leichen/courses/COMP5331/ Instructor: Dr. Lei Chen TA: Mr. Zheng Liu Venue and Time: 5583, M/W 10:30am-11:50am Reference books/materials: Papers

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COMP5331: Knowledge Discovery and Data Mining

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  1. COMP5331: Knowledge Discovery and Data Mining Instructor: Lei Chen 1

  2. Course Details • Course URL: http://www.cse.ust.hk/~leichen/courses/COMP5331/ • Instructor: Dr. Lei Chen • TA: Mr. Zheng Liu • Venue and Time: 5583, M/W 10:30am-11:50am • Reference books/materials: • Papers • Data Mining: Concepts and Techniques. Jiawei Han and Micheline Kamber. Morgan Kaufmann Publishers (3rd edition) • Introduction to Data Mining. Pang-Ning Tan, Michael Steinbach, Vipin Kumar Boston : Pearson Addison Wesley (2006)

  3. Area • DB or AI • This course can count towards one of the areas ONLY and cannot be double counted towards the required credits

  4. Course Details • Grading Scheme: • Project 40% (10% presentation +30% final report+code) • Midterm Exam 30% • Final Exam 40%

  5. Project Presentation Marking • Project Presentation Score Bonus Marks • Project Presentation Scores will be given by the audience (5%) and the instructor (5%) • Participant and Marking Bonus: all the students are strongly encouraged to attend project presentation sessions and give marks to the presenter (the score sheet will be distributed at the beginning of the session). I will give you bonus 0.25 mark for each filled score sheet. • Question Bonus: all the students are encouraged to ask questions during the Question/Answer session after each presentation. Each student is allowed to ask one question in each paper presentation. For each asked question, I will give you bonus 0.5 mark. You can staple your bonus coupon which we will give you on your filled score sheet. • Please note, the questions like “Can you explain more?”, “I cannot understand, can you repeat?” will not be counted. For each paper presentation’s Q/A session, at most 3 questions are allowed to ask. Data Mining: Concepts and Techniques

  6. Project • Each project is completed by a group. • Each group has no more than two people. • The duration of each presentation depends on the class size. • It will be announced soon.

  7. Project • Project Type (One of the following) • Survey • Implementation-oriented Project • Research-oriented Project

  8. Project • Project Type (One of the following) • Survey • Implementation-oriented Project • Research-oriented Project • Proposal • Presentation • Final report Full Score = 80% Full Score = 90% • Proposal • Presentation • Final report • Coding • Proposal • Presentation • Final report (containing your proposed methodology) • Coding (if any) Full Score = 100% 8

  9. Project • Project Topic • Some pre-selected topics/papers • Your own choice • For fairness, please do not choose the topic which is closely related to your own research

  10. Exams • You are allowed to bring a calculator with you. • Please remember to prepare a calculator for the exam • Midterm Exam will be held on Oct 12th, 2016 • Final Exam will be held on the second week of Dec, 2016

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