Information Retrieval Techniques Cse 8th Sem Syllabus for BE 2017 Regulation Anna Univ (Professional Elective V) detail syllabus for Computer Science & Engineering (Cse), 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 cse 8th sem syllabus for be 2017 regulation anna univ you can visit Cse 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 Cse, 2017 regulation do visit Cse 8th Sem syllabus for 2017 Regulation.
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