Pattern Recognition Telecom 7th Sem Syllabus for VTU BE 2017 Scheme (Professional Elective-4)

Pattern Recognition detail syllabus for Telecommunication Engineering (Telecom), 2017 scheme is taken from VTU official website and presented for VTU students. The course code (17EC753), and for exam duration, Teaching Hr/week, Practical Hr/week, Total Marks, internal marks, theory marks, duration and credits do visit complete sem subjects post given below.

For all other telecom 7th sem syllabus for be 2017 scheme vtu you can visit Telecom 7th Sem syllabus for BE 2017 Scheme VTU Subjects. For all other Professional Elective-4 subjects do refer to Professional Elective-4. The detail syllabus for pattern recognition is as follows.

Course Objectives:

The objectives of this course are to:

• Introduce mathematical tools needed for Pattern Recognition
• Impart knowledge about the fundamentals of Pattern Recognition.
• Provide knowledge of recognition, decision making and statistical learning problems
• Introduce parametric and non-parametric techniques, supervised learning and clustering concepts of pattern recognition

Module 1
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Module 2

Data Transformation and Dimensionality Reduction: Introduction, Basis Vectors, The Karhunen Loeve (KL) Transformation, Singular Value Decomposition, Independent Component Analysis (Introduction only). Nonlinear Dimensionality Reduction, Kernel PCA.

Module 3

Estimation of Unknown Probability Density Functions: Maximum Likelihood Parameter Estimation, Maximum a Posteriori Probability estimation, Bayesian Interference, Maximum Entropy Estimation, Mixture Models, Naive-Bayes Classifier, The Nearest Neighbor Rule.

Module 4
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Module 5

Nonlinear Classifiers: The XOR Problem, The two Layer Perceptron, Three Layer Perceptron, Back propagation Algorithm, Basic Concepts of Clustering, Introduction to Clustering , Proximity Measures.

Course Outcomes:

At the end of the course, students will be able to:

• Identify areas where Pattern Recognition and Machine Learning can offer a solution.
• Describe the strength and limitations of some techniques used in computational Machine Learning for classification, regression and density estimation problems
• Describe genetic algorithms, validation methods and sampling techniques
• Describe and model data to solve problems in regression and classification
• Implement learning algorithms for supervised tasks

Text Books:

Pattern Recognition: Sergios Theodoridis, Konstantinos Koutroumbas, Elsevier India Pvt. Ltd (Paper Back), 4th edition.

Reference Books:

1. The Elements of Statistical Learning: Trevor Hastie, Springer-Verlag New York, LLC (Paper Back), 2009.
2. Pattern Classification: Richard O. Duda, Peter E. Hart, David G. Stork. John Wiley & Sons, 2012.
3. Pattern Recognition and Image Analysis Earl Gose: Richard Johnsonbaugh, Steve Jost, ePub eBook.

For detail syllabus of all other subjects of BE Telecom, 2017 regulation do visit Telecom 7th Sem syllabus for 2017 Regulation.

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