Information Retrieval detailed syllabus for Information Technology (IT) for 2019 regulation curriculum has been taken from the Anna Universities official website and presented for the IT students. For course code, course name, number of credits for a course and other scheme related information, do visit full semester subjects post given below.
For Information Technology 6th Sem scheme and its subjects, do visit IT 6th Sem 2019 regulation scheme. For Professional Elective-II scheme and its subjects refer to IT Professional Elective-II syllabus scheme. The detailed syllabus of information retrieval is as follows.
Course Objective:
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Unit I
Introduction
Introduction – Goals and History of IR – The Impact of the Web on IR – The Role of Artificial Intelligence (AI) in IR – Basic IR Models – Boolean and Vector Space Retrieval Models -Ranked Retrieval – Text similarity metrics – TF-IDF (term frequency/inverse document frequency) Weighting – Cosine Similarity.
Suggested Activities:
- Understanding the basics of IR.
- Study of other retrieval models.
- Practical – Implementation of the retrieval model with Lemur Tool kit and test the performance of different retrieval algorithms.
Suggested Evaluation Methods:
- Quizzes on IR and other retrieval models.
- Assignments on retrieval models.
Unit II
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Unit III
Metrics
Experimental Evaluation of IR – Performance metrics Recall, Precision and F measure -Evaluations on Benchmark Text Collections – Text Representation – Word Statistics – Zipf”s Law – Porter Stemmer – Morphology – Index Term Selection using Thesauri -Metadata and Markup Languages – Web Search Engines – Spidering – Metacrawlers – Directed Spidering – Link Analysis Shopping Agents.
Suggested Activities:
- Practical – Implementation of evaluation metrics.
- Study and implementation of PageRank algorithm.
- Study of web page duplicate detection technique.
Suggested Evaluation Methods:
- Tutorials on web search and crawling.
- Quizzes on precision, recall and f-measure.
- Assignments on web search engines.
Unit IV
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Text Books:
Categorization and Clustering – Categorization Algorithms – Naive Bayes – Decision Trees and Nearest Neighbor – Clustering Algorithms – Agglomerative Clustering – k Means – Expectation Maximization (EM) – Applications to Information Filtering – Organization and Relevance Feedback.
Suggested Activities:
- Study of different classification techniques and its uses in different applications.
- Practical – Implementation of classification and clustering techniques with WEKA tool.
- Assignments on clustering algorithms.
Suggested Evaluation Methods:
- Quizzes on different categorization and clustering methods.
- Exercise on categorization and clustering algorithms for real time applications.
Unit V
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Course Outcome:
On completion of the course, the student will be able to:
- Build an Information Retrieval system using the available tools.
- Apply indexing and query expansion techniques for efficient retrieval.
- Apply performance metrics to validate any information retrieval system.
- Apply machine learning techniques for text classification and clustering for efficient Information Retrieval.
- Design and analyze the Web content structures.
- Design and implement recommender and information extraction system.
Text Books:
- Christopher D. Manning, Prabhakar Raghavan, Hinrich Schutze,” Introduction to Information Retrieval”, Cambridge University Press, 2008.
- Ricci, F. Rokach, L. Shapira, B. Kantor, P.B. “Recommender Systems Handbook”, Springer, 2011.
References:
For the complete syllabus, results, class timetable, and many other features kindly download the iStudy App
It is a lightweight, easy to use, no images, and no pdfs platform to make students’s lives easier..
For detailed syllabus of all the other subjects of Information Technology 6th Sem, visit IT 6th Sem subject syllabuses for 2019 regulation.
For all Information Technology results, visit Anna University IT all semester results direct link.