Prerequisites: Data Structures Course Objectives:
To learn the important concepts and algorithms in IRS
To understand the data/file structures that are necessary to design, and implement information
retrieval (IR) systems. Course Outcomes:
Ability to apply IR principles to locate relevant information large collections of data
Ability to design different document clustering algorithms
Implement retrieval systems for web search tasks.
Design an Information Retrieval System for web search tasks. UNIT – I
Introduction to Information Retrieval Systems: Definition of Information Retrieval System, Objectives of
Information Retrieval Systems, Functional Overview, Relationship to Database Management Systems,
Digital Libraries and Data Warehouses.Information Retrieval System Capabilities: Search Capabilities, Browse Capabilities, Miscellaneous
Capabilities. UNIT – II
Cataloging and Indexing: History and Objectives of Indexing, Indexing Process, Automatic Indexing,
Information Extraction.Data Structure: Introduction to Data Structure, Stemming Algorithms, Inverted File Structure, N-Gram Data Structures, PAT Data Structure, Signature File Structure, Hypertext and XML Data Structures,Hidden Markov Models. UNIT – III
Automatic Indexing: Classes of Automatic Indexing, Statistical Indexing, Natural Language, Concept
Indexing, Hypertext Linkages.Document and Term Clustering: Introduction to Clustering, Thesaurus Generation, Item Clustering,Hierarchy of Clusters. UNIT – IV
User Search Techniques: Search Statements and Binding, Similarity Measures and Ranking, Relevance Feedback, Selective Dissemination of Information Search, Weighted Searches of Boolean Systems, Searching the INTERNET and Hypertext.Information Visualization: Introduction to Information Visualization, Cognition and Perception, Information Visualization Technologies. UNIT – V
Text Search Algorithms: Introduction to Text Search Techniques, Software Text Search Algorithms,Hardware Text Search Systems.Multimedia Information Retrieval: Spoken Language Audio Retrieval, Non-Speech Audio Retrieval,Graph Retrieval, Imagery Retrieval, Video Retrieval. TEXT BOOK:
1. Information Storage and Retrieval Systems – Theory and Implementation, Second Edition,
Gerald J. Kowalski, Mark T. Maybury, Springer REFERENCE BOOKS:
1. Frakes, W.B., Ricardo Baeza-Yates: Information Retrieval Data Structures and Algorithms,
Prentice Hall, 1992.
2. Information Storage & Retrieval By Robert Korfhage – John Wiley & Sons.
3. Modern Information Retrieval By Yates and Neto Pearson Education.
CSE-AIML
SEMESTER
SUBJECT CODE
SUBJECT
Lession Plan Lecturer Notes & Question Bank
SYLLABUS
I
MA101BS
Mathematics – I
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I
AP102BS
Applied Physics
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I
CS103ES
Programming for Problem Solving
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I
ME104ES
Engineering Graphics
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I
AP105BS
Applied Physics Lab
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I
CS106ES
Programming for Problem Solving Lab
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I
MC109ES
Environmental Science
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II
MA201BS
Mathematics – II
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II
CH202BS
Chemistry
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II
EE203ES
Basic Electrical Engineering
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II
ME205ES
Engineering Workshop
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II
EN205HS
English
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II
CH206BS
Engineering Chemistry Lab
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II
EN207HS
English Language and Communication Skills Lab
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II
EE208ES
Basic Electrical Engineering Lab
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II-I
CS310PC
Discrete Mathematics
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II-I
CS302PC
Data Structures
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II-I
MA313BS
Mathematical and Statistical Foundations
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II-I
CS304PC
Computer Organization and Architecture
II-I
CS311PC
Python Programming
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II-I
SM306MS
Business Economics & Financial Analysis
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II-I
CS307PC
Data Structures Lab
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II-I
CS312PC
Python Programming Lab
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II-I
MC309
Gender Sensitization Lab
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II-II
CS416PC
Formal Language and Automata Theory
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II-II
CS417PC
Software Engineering
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II-II
CS403PC
Operating Systems
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II-II
CS404PC
Database Management Systems
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II-II
CS412PC
Object Oriented Programming using Java
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II-II
CS406PC
Operating Systems Lab
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II-II
CS407PC
Database Management Systems Lab
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II-II
CS408PC
Java Programming Lab
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II-II
MC409
Constitution of India
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III-I
Design and Analysis of Algorithms
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III-I
Machine Learning
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III-I
Computer Networks
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III-I
Compiler Design
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III-I
Graph Theory (PE1)
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III-I
Introduction to Data Science(PE1)
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III-I
Web Programming(PE1)
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III-I
Image Processing(PE1)
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III-I
Computer Graphics(PE1)
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III-I
Software Testing Methodologies(PE2)
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III-I
Information Retrieval Systems(PE2)
III-I
Pattern Recognition(PE2)
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III-I
Computer Vision and Robotics(PE2)
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III-I
Data Warehousing and Business Intelligence(PE2)
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III-I
Machine Learning Lab
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III-I
Computer Networks Lab
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III-I
Advanced Communication Skills Lab
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III-I
Intellectual Property Rights
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III-II
Artificial Intelligence
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III-II
DevOps
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III-II
Natural Language Processing
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III-II
Internet of Things(PE3)
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III-II
Data Mining(PE3)
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III-II
Scripting Languages(PE3)
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III-II
Mobile Application Development(PE3)
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III-II
Cryptography and Network Security(PE3)
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III-II
Artificial Intelligence and Natural Language
Processing Lab
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