Information Retrieval
NUS SoC, 2016/2017, Semester II Video Conferencing Room (COM1 02 VCRm) / Fridays 10:00-12:00
Module Description, Aims and Objectives:
Module Description, Aims and Objectives: 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.
Starting this semester, I will also be maintaining a Facebook page (accessible from the FB link on the top menu) for this course across cohorts. Current students and alumni are welcome to contribute news and items of potential interest to the page (i.e., IR news, job openings specific to IR).
N.B. We will be teaching and using the Python programming
language throughout this class.
Updated We will using Python 2.7.x
or Python 3.4.x, as these versions also work for the NLTK
library.
Course Characteristics:
- Modular credits: 4.
- Prerequisites:: CS 2010 Data Structures and Algorithms II or its equivalent. Important: 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, <kanmy@comp.nus.edu.sg> Office: AS6 05-12 (x61885).
Teaching Assistants: Ashish DANDEKAR, <ashishdandekar@u.nus.edu>
Office hours are held (before and after class) on Fridays after class, but more commonly 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.
- Workload: (2-1-0-5-2) Translation:
2 lecture hours per week
1 hour of tutorials or labs per week
5 hours for projects, assignments, fieldwork, etc. per week
2 hours for preparatory work by a student per week - Textbooks:
- Required: Christopher D. Manning, Prabhakar Raghavan and
Hinrich Schütze, Introduction
to Information Retrieval, Cambridge University Press. 2008.
[ Check LINC for book ] - Recommended: Steven Bird, Ewan Klein and Edward Loper, Natural language processing with
Python, O'Reilly. 2009.
[ Check 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 ]
- Required: Christopher D. Manning, Prabhakar Raghavan and
Hinrich Schütze, Introduction
to Information Retrieval, Cambridge University Press. 2008.
- Tutorials: Note: There will only be
five or six tutorial sessions; each tutorial is on a subject
related to a homework assignment, and the tutorials are only
held in place every other week, currently scheduled for Tuesday afternoon.
- Tutorial 1: Tuesdays 16:00-17:00 (SR5; COM1 #02-01)
- Tutorial 2: Tuesdays 17:00-18:00 (SR5; COM1 #02-01)
- Tutorial 3: Tuesdays 14:00-15:00 (SR5; COM1 #02-01)
- Tutorial 4: Tuesdays 12:00-13:00 (SR6; COM1 #02-03)
- Final Exam: Tuesday, 25 April 2017, 9:00-11:00 Morning, Venue: SR1 (COM1 #02-06). This is an open-book exam.