CS 410

CS 410 - Text Information Systems

Fall 2024

Title	Rubric	Section	CRN	Type	Hours	Times	Days	Location	Instructor
Text Information Systems	CS410	CSP	79900	PKG	3	-			Pablo D Robles Granda
Text Information Systems	CS410	CSP	79900	PKG	3	1400 - 1515	F	ARR Illini Center	Pablo D Robles Granda
Text Information Systems	CS410	DSO	67393	ONL	4	-			ChengXiang Zhai
Text Information Systems	CS410	MC3	79328	PKG	3	1400 - 1515	F	ARR Illini Center	Pablo D Robles Granda
Text Information Systems	CS410	MC3	79328	PKG	3	-			Pablo D Robles Granda
Text Information Systems	CS410	MC4	71013	PKG	4	-			Pablo D Robles Granda
Text Information Systems	CS410	MC4	71013	PKG	4	1400 - 1515	F	ARR Illini Center	Pablo D Robles Granda
Text Information Systems	CS410	TGR	78821	LEC	3	0930 - 1045	T R	100 Gregory Hall	Pablo D Robles Granda
Text Information Systems	CS410	TUG	78820	LEC	3	0930 - 1045	T R	100 Gregory Hall	Pablo D Robles Granda

See full schedule from Course Explorer

Web Page

https://sites.google.com/illinois.edu/cs410textinformationsystems?pli=1&authuser=1

Official Description

Theory, design, and implementation of text-based information systems. Text analysis, retrieval models (e.g., Boolean, vector space, probabilistic), text categorization, text filtering, clustering, retrieval system design and implementation, and applications to web information management. Course Information: 3 undergraduate hours. 3 or 4 graduate hours. Prerequisite: CS 225.

Course Director

Chengxiang Zhai

Text(s)

ChengXiang Zhai and Sean Massung. 2016. Text Data Management and Analysis: a Practical Introduction to Information Retrieval and Text Mining. Association for Computing Machinery and Morgan & Claypool, New York, NY, USA. https://dl.acm.org/citation.cfm?id=2915031

Learning Goals

Be able to explain the basic concepts and principles of text information systems, such as push vs. push information access, probability ranking principle, and generative models. (1), (2), (6 )

Be able to explain how key algorithms for information retrieval, Web search, and text data mining work and compare them. (1), (2), (6 )

Be able to evaluate a text information system using various metrics such as precision, recall, mean average precision, and nDCG (2 ), (6)

Be able to modify a search engine to improve accuracy (1), (2), (6)

Be able to apply knowledge of text information systems to solve a real-world problem with a course project (1), (2), (3), (5), (6)

Topic List

Background & General Introduction

Information Retrieval Models

Evaluation

Web search

Recommender systems

Text data mining

Course Project

Assignments

Last updated

2/2/2019by ChengXiang Zhai