Ian H. Witten and Eibe Frank, Data Mining: Practical Machine Learning Tools and Techniques with Java Implementations, Morgan Kaufmann, (2000).
Data Mining and Knowledge Discovery, Intelligent Data Analysis, Machine Learning, Artificial Intelligence, Knowledge-Based Systems, Applied Intelligence, Journal of Artificial Intelligence Research, IEEE Transactions on Pattern Analysis and Machine Intelligence.
WEEK DAYS TOPICS 1 Feb 8, 10 Introduction to Data Mining 2 Feb 15, 17 Seminar 3 Feb 22, 24 Seminar 4 Feb 29, 2 Seminar 5 Mar 7, 9 Seminar 6 Mar 14 Seminar 7 Mar 21, 23 Seminar 8 Mar 28, 30 Seminar 9 Apr 4, 6 Seminar 10 Apr 11, 13 Seminar 11 Apr 18, 20 Seminar 12 Apr 25, 27 Seminar 13 May 2, 4 Seminar 14 May 9, 11 Seminar 15 May 16, 18 Seminar
Other than his/her presentation, each student is expected to read the papers to be presented by others before the presentation. After each presentation, we will have a discussion session. Each student will be graded in his/her participation to the seminar.
Students are referred to the paper "How to Present a Paper in Theoretical Computer Science: A Speaker's Guide for Students" for a successful presentation.
Term Project: Each student will conduct an experiment, testing new ideas preferably on the area of their presentation topic. Then, the student will prepare a short paper (at most 8 pages) reporting his/her experiment(s) along with the interpretation of the results and pointers for further research. The paper should have the quality of, at least, a national symposium paper. Three copies of papers will be submitted to the instructor by May 1. Each paper will be reviewed by two randomly selected classmates. The papers will be distributed for reviewing on May 2. Each reviewer will put his/her comments and suggestions on the paper and return them back to the instructor by May, 9. The papers with peer reviews and instructor's remarks will then be returned to the authors on May 16. The authors will revise (if necessary) their papers in the light of the reviews, and submit the final copies by May, 22 to the instructor.
Students are referred to the paper "How to give a good research talk" for a successful presentation of your own work.
Presentations during seminars will be made using the datashow equipment connected to a PC that will be made available in the class. Therefore, the students are asked to prepare their presentations using the PowerPoint® program.
Seminar: 50% (Presentation: 40% + Participation: 10%) Workshop: 50% (Paper: 40% + Review: 10%)