Syllabus B Tech Computer Science Fifth Semester Pattern Recognition CS503

The concepts developed in this course will aid in quantification of several concepts in Computer Science Engineering that have been introduced at the Engineering courses. Technology is being increasingly based on the latest Syllabus B Tech Computer Science Fifth Semester Pattern Recognition CS503 is given here.

The objective of this course “Syllabus B Tech Computer Science Fifth Semester Pattern Recognition CS503“ is to develop ability and gain insight into the process of problem-solving, with emphasis on thermodynamics. Specially in following manner: Apply conservation principles (mass and energy) to evaluate the performance of simple engineering systems and cycles. Evaluate thermodynamic properties of simple homogeneous substances. Analyze processes and cycles using the second law of thermodynamics to determine maximum efficiency and performance. Discuss the physical relevance of the numerical values for the solutions to specific engineering problems and the physical relevance of the problems in general and Critically evaluate the validity of the numerical solutions for specific engineering problems. More precisely, the objectives are:

To enable young technocrats to acquire mathematical knowledge to understand Laplace transformation, Inverse Laplace transformation and Fourier Transform which are used in various branches of engineering.
To introduce effective mathematical tools for the Numerical Solutions algebraic and transcendental equations.
To acquaint the student with mathematical tools available in Statistics needed in various field of science and engineering.

CS 503 – Pattern Recognition

Unit 1

Introduction – Definitions, data sets for Pattern, Application Areas and Examples of pattern recognition, Design principles of pattern recognition system, Classification and clustering, supervised Learning, unsupervised learning and adaptation, Pattern recognition approaches, Decision Boundaries, Decision region , Metric spaces, distances.

Unit 2

Classification: introduction, application of classification, types of classification, decision tree, naïve bayes, logistic regression , support vector machine, random forest, K Nearest Neighbour Classifier and variants, Efficient algorithms for nearest neighbour classification, Different Approaches to Prototype Selection, Combination of Classifiers, Training set, test set, standardization and normalization.

Unit 3

Different Paradigms of Pattern Recognition, Representations of Patterns and Classes, Unsupervised Learning & Clustering: Criterion functions for clustering, Clustering Techniques: Iterative square -error partitional clustering – K means, hierarchical clustering, Cluster validation.

Unit 4

introduction of feature extraction and feature selection, types of feature extraction , Problem statement and Uses, Algorithms – Branch and bound algorithm, sequential forward / backward selection algorithms, (l,r) algorithm.

Unit 5

Recent advances in Pattern Recognition, Structural PR, SVMs, FCM, Soft computing and Neuro-fuzzy techniques, and real-life examples, Histograms rules, Density Estimation, Nearest Neighbor Rule, Fuzzy classification.

Books Recommended

1. Richard O. Duda, Peter E. Hart and David G. Stork, “Pattern Classification”, 2nd Edition, John Wiley, 2006.
2. C. M. Bishop, “Pattern Recognition and Machine Learning”, Springer, 2009.
3. S. Theodoridis and K. Koutroumbas, “Pattern Recognition”, 4th Edition, academic Press, 2009.
4. Robert Schalkoff, “pattern Recognition: statistical, structural and neural approaches”, JohnWiley & sons , Inc, 2007.

Syllabus B Tech Computer Science Fifth Semester Pattern Recognition CS503

CS 503 – Pattern Recognition

Books Recommended

Related Posts

Syllabus B Tech Computer Science Eighth Semester Cyber Law And Ethics CS8004

Syllabus B Tech Computer Science Eighth Semester Computer Peripherals And Interfaces CS8003

Syllabus B Tech Computer Science Eighth Semester Augmented And Virtual Reality CS8004