Pattern Recognition detailed Syllabus for Information Technology (IT), R18 regulation has been taken from the JNTUH official website and presented for the students affiliated to JNTUH course structure. For Course Code, Subject Names, Theory Lectures, Tutorial, Practical/Drawing, Credits, and other information do visit full semester subjects post given below. The Syllabus PDF files can also be downloaded from the universities official website.
For all the other IT 3rd Year 1st Sem Syllabus for B.Tech R18 Regulation JNTUH, visit Information Technology 3rd Year 1st Sem R18 Scheme.
For all the (Professional Elective-II) subjects refer to Professional Elective-II Scheme. The detail syllabus for pattern recognition is as follows.
Pre-requisite:
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Course Objectives:
- This course introduces fundamental concepts, theories, and algorithms for pattern recognition and machine learning.
- Topics include: Pattern Representation, Nearest Neighbor Based Classifier, Bayes Classifier, Hidden Markov Models, Decision Trees, Support Vector Machines, Clustering, and an application of hand-written digit recognition.
Course Outcomes:
- Understand the theory, benefits, inadequacies and possible applications of various machine learning and pattern recognition algorithms
- Identify and employ suitable machine learning techniques in classification, pattern recognition, clustering and decision problems.
Unit I
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Unit II
Nearest Neighbor Based Classifier: Nearest Neighbor Algorithm, Variants of the NN Algorithm use of the Nearest Neighbor Algorithm for Transaction Databases, Efficient Algorithms, Data Reduction, Prototype Selection. Bayes Classifier: Bayes Theorem, Minimum Error Rate Classifier, Estimation of Probabilities, Comparison with the NNC, Naive Bayes Classifier, Bayesian Belief Network.
Unit III
Hidden Markov Models: Markov Models for Classification, Hidden Morkov Models, Classification using HMMs. Decision Trees: Introduction, Decision Tree for Pattern Classification, Construction of Decision Trees, Splitting at the Nodes, Overfitting and Pruning, Examples of Decision Tree Induction.
Unit IV
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Unit V
Clustering: Why is Clustering Important, Hierarchical Algorithms, Partitional Clustering, Clustering Large Data Sets. An Application-Hand Written Digit Recognition: Description of the Digit Data, Preprocessing of Data, Classification Algorithms, Selection of Representative Patterns, Results.
Text Books:
- Pattern Recognition: An Algorithmic Approach: Murty, M. Narasimha, Devi, V. Susheela, Spinger Pub,1st Ed.
Reference Books:
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 detail Syllabus of all other subjects of B.Tech 3rd Year 1st Sem Information Technology, visit IT 3rd Year 1st Sem Syllabus Subjects.
For all B.Tech results, visit JNTUH B.Tech all years, and semester results from direct links.