DON BOSCO INSTITUTE OF TECHNOLOGY DEPARTMENT OF INFORMATION SCIENCE & ENGINEERING Course Code Course Title Core/Elective Prerequisite 10IS664 Pattern Recognition Core Probability Theory, Linear Algebra ,Multivariate Statistic Course Objectiv es Contact Hours L T P 4 1 - Total Hrs/ Sessions 52 On completion of this subject, students will be expected to: Explain and compare a variety of pattern classification, structural pattern recognition, and pattern classifier combination 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 such as document analysis and recognition. Implement simple pattern classifiers, classifier combinations, and structural pattern recognizers. Syllabus PART – A UNIT – 1 6 Hours Introduction: Machine perception, an example; Pattern Recognition System; The Design Cycle; Learning and Adaptation. UNIT – 2 7 Hours Bayesian Decision Theory: Introduction, Bayesian Decision Theory; Continuous Features, Minimum error rate, classification, classifiers, discriminant functions, and decision surfaces; The normal density; Discriminant functions for the normal density. UNIT – 3 7 Hours Maximum-likelihood and Bayesian Parameter Estimation: Introduction; Maximum-likelihood estimation; Bayesian Estimation; Bayesian parameter estimation: Gaussian Case, general theory; Hidden Markov Models. UNIT – 4 6 Hours Non-parametric Techniques: Introduction; Density Estimation; Parzen windows; kn – NearestNeighbor Estimation; The Nearest- Neighbor Rule; Metrics and Nearest-Neighbor Classification. PART – B UNIT – 5 7 Hours Linear Discriminant Functions: Introduction; Linear Discriminant Functions and Decision Surfaces; Generalized Linear Discriminant Functions; The Two-Category Linearly Separable case; Minimizing the Perception Criterion Functions; Relaxation Procedures; Non-separable Behavior; Minimum SquaredError procedures; The Ho-Kashyap procedures. UNIT – 6 6 Hours Stochastic Methods: Introduction; Stochastic Search; Boltzmann Learning; Boltzmann Networks and Graphical Models; Evolutionary Methods. UNIT – 7 6 Hours Non-Metric Methods: Introduction; Decision Trees; CART; Other Tree Methods; Recognition with Strings; Grammatical Methods. UNIT – 8 7 Hours Unsupervised Learning and Clustering: Introduction; Mixture Densities and Identifiability; MaximumLikelihood Estimates; Application to Normal Mixtures; Unsupervised Bayesian Learning; Data Description and Clustering; Criterion Functions for Clustering List of Text Books Text Books: 1. Richard O. Duda, Peter E. Hart, and David G. Stork: Pattern Classification, 2nd Edition, WileyInter science, 2001. List of Reference Books DON BOSCO INSTITUTE OF TECHNOLOGY DEPARTMENT OF INFORMATION SCIENCE & ENGINEERING 1. Earl Gose, Richard Johnsonbaugh, Steve Jost: Pattern Recognition and Image Analysis, PHI, Indian Reprint 2008. List of URLs-Text Books, Notes, Multimedia Content, etc 1. http://www.cs.rit.edu/~rlaz/20092/ 2. http://www.cs.rit.edu/~rlaz/prec20092/Resources.html. On completion of this subject students will be able to: Formulate and describe various applications in pattern recognition Understand the Bayesian approach to pattern recognition Be able to mathematically derive, construct, and utilize Bayesian-based classifiers and non-Bayesian classifiers both theoretically and practically. Course Understand basic concepts such as the central limit theorem, the curse of Outcome dimensionality, the bias-variance dilemma, and cross-validation Validate and assess different clustering techniques Apply various dimensionality reduction methods whether through feature selection or feature extraction Assess classifier complexity and regularization parameters Be able to combine various classifiers using fixed rules or trained combiners and boost their performance Internal Assessment Marks:(50) 3 Internal Assessment Tests are conducted during the semester and marks allotted based on average of best two performances and reduced to 25 marks. External Marks: (100) Students have to answer 5 questions out of 8 questions choosing at least 2 out of 4 questions from PART – A and at least 2 out of 4 questions from PART – B and 1 question from either of the part. Program Outcomes mapping with Course Subject Name PATTERN RECOGNITION Note: a b c d Program Outcomes e f g h i j k l 4 4 2 4 4 3 2 4 4 4 = Strong Contribution 3 = Average Contribution 2 1 1 2 = Weak Contribution 1 = No Contribution Program Educational Objectives mapping with Course Subject Name PATTERN RECOGNITION Note: 4 = Strong Contribution PEO1 4 Program Educational Objectives PEO2 PEO3 PEO4 3 3 = Average Contribution 4 2 PEO5 4 2 = Weak Contribution 1 = No Contribution

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