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

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

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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 !!

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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 !).

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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.

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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.

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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.