Information Retrieval Techniques C&C 8th Sem Syllabus for BE 2017 Regulation Anna Univ (Professional Elective V) detail syllabus for Computer & Communication Engineering (C&C), 2017 regulation is collected from the Anna Univ official website and presented for students of Anna University. The details of the course are: course code (CS8080), Category (PE), Contact Periods/week (3), Teaching hours/week (3), Practical Hours/week (0). The total course credits are given in combined syllabus.
For all other c&c 8th sem syllabus for be 2017 regulation anna univ you can visit C&C 8th Sem syllabus for BE 2017 regulation Anna Univ Subjects. For all other Professional Elective V subjects do refer to Professional Elective V. The detail syllabus for information retrieval techniques is as follows.
Course Objective:
- To understand the basics of Information Retrieval.
- To understand machine learning techniques for text classification and clustering.
- To understand various search engine system operations.
- To learn different techniques of recommender system.
Unit I
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Unit II
Modeling and Retrieval Evaluation
Basic IR Models – Boolean Model – TF-IDF (Term Frequency/Inverse Document Frequency) Weighting – Vector Model – Probabilistic Model – Latent Semantic Indexing Model – Neural Network Model – Retrieval Evaluation – Retrieval Metrics – Precision and Recall – Reference Collection -User-based Evaluation – Relevance Feedback and Query Expansion – Explicit Relevance Feedback.
Unit III
Text Classification and Clustering
A Characterization of Text Classification – Unsupervised Algorithms: Clustering – Naive Text Classification – Supervised Algorithms – Decision Tree – k-NN Classifier – SVM Classifier – Feature Selection or Dimensionality Reduction – Evaluation metrics – Accuracy and Error – Organizing the classes – Indexing and Searching – Inverted Indexes – Sequential Searching – Multi-dimensional Indexing.
Unit IV
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Unit V
Recommender System
Recommender Systems Functions – Data and Knowledge Sources – Recommendation Techniques -Basics of Content-based Recommender Systems – High Level Architecture – Advantages and Drawbacks of Content-based Filtering – Collaborative Filtering – Matrix factorization models -Neighborhood models.
Course Outcome:
Upon completion of the course, the students will be able to:
- Use an open source search engine framework and explore its capabilities
- Apply appropriate method of classification or clustering.
- Design and implement innovative features in a search engine.
- Design and implement a recommender system.
Text Books:
- Ricardo Baeza-Yates and Berthier Ribeiro-Neto, Modern Information Retrieval: The Concepts and Technology behind Search, Second Edition, ACM Press Books, 2011.
- Ricci, F, Rokach, L. Shapira, B.Kantor, Recommender Systems Handbook, First Edition, 2011.
References:
- C. Manning, P. Raghavan, and H. Schutze, Introduction to Information Retrieval, Cambridge University Press, 2008.
- Stefan Buettcher, Charles L. A. Clarke and Gordon V. Cormack, Information Retrieval: Implementing and Evaluating Search Engines, The MIT Press, 2010.
For detail syllabus of all other subjects of BE C&C, 2017 regulation do visit C&C 8th Sem syllabus for 2017 Regulation.
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