Information Retrieval and Data Mining (AT71.07) Comp. Sc. and Inf. Mgmt. Asian Institute of Technology - PowerPoint PPT Presentation

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Information Retrieval and Data Mining (AT71.07) Comp. Sc. and Inf. Mgmt. Asian Institute of Technology

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Title: Information Retrieval and Data Mining (AT71.07) Comp. Sc. and Inf. Mgmt. Asian Institute of Technology


1
Information Retrieval and Data Mining
(AT71.07)Comp. Sc. and Inf. Mgmt.Asian
Institute of Technology
2
Course OverviewPage 1
  • Instructor Prof. Sumanta Guha
  • Office 104 CSIM Building
  • Email guha_at_ait.ac.th
  • Telephone 5714 i
  • Credits 3(3-0)
  • Prerequisite
  • Officially none
  • Course Website http//www.cs.ait.ac.th/guha/IRDM
    /

3
Course OverviewPage 2
  • Class times Mon. Th. 1400-1530
  • Discussion Group Yahoo group ait_csim_irdm
  • WWW http//groups.yahoo.com/group/ait_csim_ir
    dm/
  • Email ait_csim_irdm_at_yahoogroups.com
  • You must join the group!
  • Membership is currently open so anyone can join
    just go to the link above and click the join
    button. If you have a problem send me mail and I
    will invite you.
  • Important Its good for everyone to post
    questions and comments to the discussion group!!
    Then, everybody benefits from the interaction.
    Announcements by the instructor will always be
    posted to the group.
  • However, if you wish to see me in my office
    you are always welcome (provided I am not busy).
    Its best to make an appointment. Note that I am
    not a morning person.
  • Please check the group frequently and please
    participate in discussions !!

4
Course OverviewPage 3
  • Textbooks (required)
  • C. D. Manning, P. Raghavan, H. Schütze (2008),
    Introduction to Information Retrieval, Cambridge
    University Press.
  • J. Han, M. Kamber, J. Pei (2011), Data Mining
    Concepts and Techniques, 3rd edition, Morgan
    Kaufmann (2nd ed. is fine !).

5
Course OverviewPage 4
  • Brief Course Outline
  • We will alternate between information retrieval
    and data mining in the early weeks one period
    each week studying IR and the other DM. Later the
    two threads will converge.
  • We will begin with Chapters 1, 2, 4, 6, from
    the IR book and Chapters 5, 6, 7, from the DM
    book. The reason for the omitted chapters is that
    they are either elementary (left to the student
    to read on her own) or dig too deep into one
    particular area (as our goal is a broad coverage
    not specialization).
  • Objectives
  • To learn the fundamental concepts of modern-day
    IRDM.
  • To become familiar with recent literature. IRDM
    is a young field so most developments are, in
    fact, recent. Therefore, using original research
    papers as source material is not only possible,
    but advised.
  • To acquire some familiarity with practical IRDM
    software, e.g., Weka.

6
Course OverviewPage 5
  • Reference Books
  • M. J. A. Berry and G. Linoff (1997), Data Mining
    Techniques For Marketing, Sales, and Customer
    Relationship Management, Wiley.
  • I. H. Witten and E. Frank (2001), Data Mining
    Practical Machine Learning Tools and Techniques,
    Morgan Kaufmann.
  • T. Soukup and I. Davidson (2002), Visual Data
    Mining Techniques and Tools for Data
    Visualization and Mining, Wiley.
  • P. Tan, M. Steinbach and V. Kumar (2005),
    Introduction to Data Mining, Addison-Wesley.
  • D. T. Larose (2006), Data Mining Methods and
    Models, Wiley.
  • B. Croft, D. Metzler, T. Strohman (2009), Search
    Engines Information Retrieval in Practice,
    Addison-Wesley.

7
Course OverviewPage 6
  • Grading System (tentative)
  • Mid-sem 40
  • Final 60
  • Enjoy the Course!
  • Be enthusiastic about the material because it is
    interesting, practical, and extremely important
    in the modern day world. Our job is to help you
    learn and enjoy the experience. We will do our
    best but we also need your help.
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