Information Retrieval
NUS SoC, AY 23/24, Semester II, Fridays 12:00-14:00
Course Description, Aims and Objectives:
Course Description, Aims and Objectives: This course 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 course also serves as the foundation for subsequent courses on the understanding, processing and retrieval of particular web media.
There is 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 using the Python 3 and the NLTK library throughout this class.
Course Characteristics:
- Modular credits: 4.
- Prerequisites: (CS2010 or its equivalent) or CS2020 or (CS2040 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.
- Staff:
- Instructor: ZHAO Jin, <zhaojin@comp.nus.edu.sg> Office: COM2-02-10 (66011083).
- Teaching Assistant: TBA
- Office hours are 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 the forum. 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)
- 2 lecture hours per week
- 1 tutorial hour 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: Stefan Buttcher, Charles L. A. Clarke and Gordon V. Cormack Information Retrieval: Implementing and Evaluating Search Engines, MIT Press. 2016.
[ Check LINC for book ]
- Required: Christopher D. Manning, Prabhakar Raghavan and
Hinrich Schütze, Introduction
to Information Retrieval, Cambridge University Press. 2008.
- Lectures: The lectures are conducted on Fridays at 12:00-14:00 at LT15 @ AS6 Level 1 (Map).
- Tutorials: The (optional) tutorials are scheduled in odd weeks (Weeks 3/5/7...). Participation marks will be awarded to the students who present their answers for discussion during the tutorials. There will be 4 tutorial groups as listed below and the venue of the tutorials is SR 9 @ COM1-02-09 (Map).
- Tutorial Group 1: Thursday 10:00-11:00
- Tutorial Group 2: Thursday 11:00-12:00
- Tutorial Group 4: Friday 10:00-11:00
- Tutorial Group 5: Friday 11:00-12:00
- Forum: The course forum is hosted on Piazza. Participation marks will be awarded to students who present their answers to the tutorial questions.
- Final Exam: The exam is scheduled on 30 Apr (Tue), 5-7pm. Venue: TBA.
