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AD-603 (C) RGPV B.Tech

Information Retrieval (IR)

The official syllabus is free to read, no account needed.

Syllabus, notes and important questions for AD-603 (C)

Syllabus units

Topics as the official RGPV syllabus lists them.

Unit 1 Introduction 9 topics
  • History of Information Retrieval
  • Components of Information Retrieval
  • Issues in Information Retrieval
  • Open source search engine frameworks
  • Impact of the web on Information Retrieval
  • Role of artificial intelligence (AI) in Information Retrieval
  • IR versus Web Search
  • Components of a search engine
  • Characterizing the web
Unit 1 notes
Unit 2 Retrieval Models and Weighting 13 topics
  • Boolean retrieval model
  • Vector space retrieval model
  • Term weighting
  • TF‑IDF weighting
  • Cosine similarity
  • Pre‑processing techniques
  • Inverted indices
  • Efficient processing with sparse vectors
  • Language‑model based IR
  • Probabilistic IR
  • Latent Semantic Indexing (LSI)
  • Relevance feedback
  • Query expansion
Unit 2 notes
Unit 3 Web Search and Crawling 11 topics
  • Web search overview
  • Web structure
  • User‑paid placement and search engine optimization
  • Web search architectures
  • Crawling techniques
  • Meta‑crawlers
  • Focused crawling
  • Web indexes
  • Near‑duplicate detection
  • Index compression
  • XML retrieval
Unit 3 notes
Unit 4 Link Analysis and Ranking 16 topics
  • Link analysis
  • Hubs and authorities
  • PageRank algorithm
  • HITS algorithm
  • Searching and ranking
  • Relevance scoring for the web
  • Similarity measures
  • Hadoop & MapReduce for IR
  • Evaluation of IR systems
  • Personalized search
  • Collaborative filtering
  • Content‑based recommendation
  • Handling the invisible web
  • Snippet generation and summarization
  • Question answering
  • Cross‑lingual retrieval
Unit 4 notes
Unit 5 Information Filtering and Text Mining 7 topics
  • Information filtering
  • Organization and relevance feedback
  • Text mining
  • Text classification
  • Text clustering
  • Categorization algorithms (Naïve Bayes, decision trees, nearest neighbor)
  • Clustering algorithms (agglomerative clustering, k‑means, expectation‑maximisation (EM))
Unit 5 notes
Read the full syllabus

Exam insights

Counted from 3 past AD-603 (C) papers.

Marks asked per paper, by unit

  • Unit 1 Introduction 26 marks
  • Unit 2 Retrieval Models and Weighting 30 marks
  • Unit 3 Web Search and Crawling 5 marks
  • Unit 4 Link Analysis and Ranking 20 marks
  • Unit 5 Information Filtering and Text Mining 45 marks

Average marks of the questions from each unit in one paper. You answer only some of them, so the total is more than the paper’s maximum.

Asked again and again

  1. Explain the concepts and techniques of Information Filtering, including its role in IR systems.

    Asked 11 times: 2023, 2024, 2025

  2. Describe the components of a search engine and illustrate their interaction with a block diagram.

    Asked 5 times: 2023, 2024, 2025

  3. Detail the pre‑processing techniques used in IR (tokenisation, stop‑word removal, stemming, etc.).

    Asked 3 times: 2023, 2024, 2025

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