Pattern Recognition 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 5th Sem scheme and its subjects, do visit IT 5th Sem 2019 regulation scheme. For Professional Elective-I scheme and its subjects refer to IT Professional Elective-I syllabus scheme. The detailed syllabus of pattern recognition is as follows.
Course Objective:
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Unit I
Pattern Classifier
Overview of Pattern Recognition – Discriminant Functions – Supervised Learning -Parametric Estimation – Maximum Likelihood Estimation – Bayes Theorem – Bayesian Belief Network, Naive Bayesian Classifier.
Suggested Activities:
- Discussion on pattern recognition applications like image classification.
- Implementation of Bayesian belief network using MatLab.
- Implementing Naive Bayesian classifier using MatLab.
Suggested Evaluation Methods:
- Quizzes on pattern recognition applications like image classification.
- Programming assignments on various pattern classifier techniques.
Unit II
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Unit III
Feature Extraction and Selection
Entropy Minimization – Karhunen Loeve Transformation – Feature Selection Through Functions Approximation – Binary Feature Selection – K-NN.
Suggested Activities:
- Implementation of K-NN in MatLab.
- Implementation of decision tree in MatLab.
Suggested Evaluation Methods:
- Quizzes on feature selection methods.
- Programming assignments on KL transformation.
Unit IV
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Unit V
Recent Advances
Fuzzy Classification: Fuzzy Set Theory, Fuzzy And Crisp Classification, Fuzzy Clustering, Fuzzy Pattern Recognition – Introduction to Neural Networks: Elementary Neural Network For Pattern Recognition, Hebbnet, Perceptron, ADALINE, Back Propagation.
Suggested Activities:
- Develop a supervised model to train neural net that uses the AND/OR/XOR gate functions.
- Create and view custom neural networks using MatLab.
Suggested Evaluation Methods:
- Quizzes on basic fuzzy and neural logic.
- Programming assignments on fuzzy classification methods.
Course Outcome:
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..
Text Books:
- Andrew Webb, “Statistical Pattern Recognition”, Arnold publishers, London, 1999.
References:
- C. M. Bishop, “Pattern Recognition and Machine Learning”, Springer, 2006.
- R. O. Duda, P. E. Hart, D. G. Stork, “Pattern Classification”, John Wiley, 2001.
- M. Narasimha Murthy, V. Susheela Devi, “Pattern Recognition”, Springer 2011.
- Menahem Friedman, Abraham Kandel, “Introduction to Pattern Recognition Statistical, Structural, Neural and Fuzzy Logic Approaches”, World Scientific publishing Co. Ltd, 2000.
- Robert J. Schalkoff, “Pattern Recognition Statistical, Structural and Neural Approaches”, John Wiley and Sons Inc., 1992.
- S. Theodoridis, K. Koutroumbas, “Pattern Recognition”, Fourth Edition, Academic Press, 2009.
For detailed syllabus of all the other subjects of Information Technology 5th Sem, visit IT 5th Sem subject syllabuses for 2019 regulation.
For all Information Technology results, visit Anna University IT all semester results direct link.