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Course Overview Page 1

Dive into the fundamental concepts of modern-day Information Retrieval and Data Mining with practical software usage. Explore recent literature and gain familiarity with the field. Course includes alternating studies on IR and DM. Grading based on mid-term and final exams.

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Course Overview Page 1

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

  2. Course OverviewPage 1 • Instructor: Adj. Prof. Sumanta Guha • Office: 104 CSIM Building • Email: guha@ait.ac.th • Telephone: +66 – (0) 2524-5714 (5714 in AIT) • 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. 14:00-15:30 • Discussion Group: Piazza: IRDM at AIT • Sign-up link: piazza.com/ait.asia/fall2019/irdm • Class link: piazza.com/ait.asia/fall2019/irdm/home • You must join the group! • Important: It’s 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). It’s best to make an appointment. Note that I am not a morning person. Please checkthe 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. • Multiple resources on the web !

  7. Course OverviewPage 6 • Grading System: • 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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