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Monday, January 30, 2012 08:49:33 AM SGT
- Updated Tutorial Session 3 on Monday afternoons.
This session of this module is over. Please refer to the current session for information.
Module Description (excerpted from the bulletin):
This module discusses the basic concepts and methods of information
retrieval including capturing, representing, storing, organizing, and
retrieving unstructured or loosely structured information. The most
well-known aspect of information retrieval is document retrieval: the
process of indexing and retrieving text documents. However, the field
of information retrieval includes almost any type of unstructured or
semi-structured data, including newswire stories, transcribed speech,
email, blogs, images, or video. Therefore, information retrieval is a
critical aspect of Web search engines. This module also serves as the
foundation for subsequent modules on the understanding, processing and
retrieval of particular web media.
N.B. We will be teaching and using the Python programming
language throughout this class. We will using Python 2.6.x (2.6.6 or 2.6.4) instead of
the updated Python 3.x, as the NLTK library that we will also be using
is currently incompatible with 3.x.
- Modular credits: 4.
- Prerequisites: CS2010 Data Structures and Algorithms II
or its equivalent. It is highly suggested to have some advanced mathematics
background such as probability and statistics, and/or linear algebra.
Exceptions to these pre-requisites can be made on a case-by-case basis
only. See instructor for details.
- Instructor: Min-Yen KAN,
Office: AS6 05-12 (x1885). Office hours are held (directly before class) on Mondays,
9:00-10:00, or by appointment. Emails to me as a default
are assumed to be public, and my replies and your anonymized
email will likely be posted to IVLE. Please let me know if you
do not want the contents of your email posted; I will be
happy to honor your requests.
- Teaching Assistant: Hadi AMIRI, <firstname.lastname@example.org>. (Office: AS6 #05-21, Media Industry Research
Lab). Hours: Thursdays, 14:00-15:00, or by appointment.
- Workload: (2-1-0-5-2) 2 lecture hours, 1 tutorial hour, 5
hours for assignments, and 2 hours preparation per week.
- Required: Christopher D. Manning, Prabhakar Raghavan and
Hinrich Schütze, Introduction
to Information Retrieval, Cambridge University Press. 2008.
LINC for book ]
- Recommended: Steven Bird, Ewan Klein and Edward Loper, Natural language processing with
Python, O'Reilly. 2009.
LINC for book ]
- Recommended: Ian H. Witten, Alistair Moffat, Timothy C.
Bell, Managing Gigabytes: Compressing and Indexing Documents and
Images, 1999. Second Edition
[ Check LINC
for book ]
- Recommended: Ricardo Baeza-Yates and Berthier Ribeiro-Neto, Modern
Information Retrieval, 1999. First Edition
[ Check LINC
for book ]
- Tutorial Sessions: Note: There will only be five
tutorial sessions; each tutorial is on a subject related to a homework
assignment, and the tutorials are held every other week. Two slots
that are back-to-back on Thursday afternoon and an additional
supplementary one on Monday right after class are currently scheduled.
- Thursdays 12:00-13:00 (COM1 #02-09)
- Thursdays 13:00-14:00 (COM1 #02-09)
- Mondays 12:00-13:00 (COM1 #02-11)
- Final Exam: Tuesday, 24 April 2012 (13:00-15:00, Venue SR3).
Note to NUS-external visitors: Welcome! If
you're a fellow I.R. course instructor looking for lecture material,
you can see the syllabus menu item on the left for a preview. Please
contact me if you'd like to use any of my material. Thanks!
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