Course Details
Subject {L-T-P / C} : EE6152 : Pattern Recognition { 3-0-0 / 3}
Subject Nature : Theory
Coordinator : Prof. Dipti Patra
Syllabus
Pattern Recognition: Feature Extraction and classification stages, Different approaches to pattern recognition. Statistical Pattern Recognition : Hypothesis testing, Linear classifiers, Parametric and nonparametric classification techniques, Unsupervised learning and clustering, Syntactic pattern recognition, Fuzzy set Theoretic approach to PR, Applications of PR : Speech and speaker recognition, Character recognition, Scene analysis.
Course Objectives
- Provide knowledge of models, methods and tools used to solve regression, <br />classification, feature selection and density estimation problems
- Provide knowledge of current research topics and issues in Pattern Recognition and <br />Machine Learning
- Provide hands-on experience in analyzing and developing solutions/algorithms <br />capable of learning
Course Outcomes
• Explain and compare a variety of pattern classification, structural pattern recognition techniques. <br />• Apply performance evaluation methods for pattern recognition, and critique comparisons of techniques made in the research literature. <br />• Apply pattern recognition techniques to real-world problems. <br />• Implement simple pattern classifiers, classifier combinations, and structural pattern recognizers.
Essential Reading
- Peter E. Hart, Richard O. Duda, David G. Stork, Pattern Classification, Wiley
- Christopher Bishop, Pattern Recognition & Machine Learning, Springer
Supplementary Reading
- T.Y. Young & King-Sun Fu, Handbook of Pattern Recognition & Image Processing, Academic Press
- Peebles, Peyton Z, Probability, Random Variables & Random Signal Principles, McGraw-Hill
Journal and Conferences
- IEEE Transaction on Pattern Analysis and Machine Intelligence, IEEE conference on Computer Vision & Pattern Recognition
- Elsevier Journal on Pattern Recognition