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.

Provide knowledge of models, methods and tools used to solve regression,
classification, feature selection and density estimation problems

Provide knowledge of current research topics and issues in Pattern Recognition and
Machine Learning

Provide hands-on experience in analyzing and developing solutions/algorithms
capable of learning

• Explain and compare a variety of pattern classification, structural pattern recognition techniques.
• Apply performance evaluation methods for pattern recognition, and critique comparisons of techniques made in the research literature.
• Apply pattern recognition techniques to real-world problems.
• Implement simple pattern classifiers, classifier combinations, and structural pattern recognizers.

Peter E. Hart, Richard O. Duda, David G. Stork, Pattern Classification, Wiley

Christopher Bishop, Pattern Recognition & Machine Learning, Springer

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

Elsevier Journal on Pattern Recognition

IEEE Transaction on Pattern Analysis and Machine Intelligence, IEEE conference on Computer Vision & Pattern Recognition