Back close

Course Detail

Course Name Pattern Recognition
Course Code 19CCE337
Program B. Tech. in Computer and Communication Engineering
Year Taught 2019


Unit 1

Introduction – Applications of pattern recognition -Probability distribution basics – Discrete distributions and Continuous distributions – Conditional probability distribution and Joint probability distribution – Statistical decision Making – Introduction – Bayes’ theorem – conditionally independent features – Naïve bayes classifier – Decision Boundaries – Unequal costs of error – Estimation of error rates.

Unit 2

Nonparametric decision making – Introduction – histograms – K nearest neighbor method – adaptive decision Boundaries – adaptive discriminant functions – minimum squared error discriminant functions – Artificial neural Networks – Logistic regression – Perceptron – Multilayer feed forward neural network – Gradient descent method – back propagation -Dimensionality Reduction Techniques – Principal component analysis – Fisher discriminant analysis.


  • Earl Gose, Richard Johnsonbaugh, Steve Jost, “Pattern Recognition and Image Analysis”, Prentice Hall India Private Limited, 2003.
  • Bishop, Christopher M, “Pattern recognition and Machine Learning”, Springer, 2006.


  • Duda, Richard O., Peter E. Hart, and David G. Stork, “Pattern classification”, John Wiley & Sons, 2012.
  • Fausett, Laurene V., “Fundamentals of neural networks: architectures, algorithms, and applications”, Vol. 3. Englewood Cliffs: Prentice-Hall, 1994.

Evaluation Pattern

Assessment Internal External
Periodical 1 (P1) 15
Periodical 2 (P2) 15
*Continuous Assessment (CA) 20
End Semester 50
*CA – Can be Quizzes, Assignment, Projects, and Reports.

Objectives and Outcomes


  • To understand the concept of pattern and the basic approach in developing pattern recognition algorithms
  • To develop prototype pattern recognition algorithms that can be applied against real-world multivariate data
  • To effectively implement pattern recognition algorithms for specific applications using simulation tools

Course Outcomes

  • CO1: Able to apply the knowledge of mathematics for obtaining solutions in pattern recognition domain
  • CO2: Able to apply various algorithms for pattern recognition
  • CO3: Able to map the pattern recognition concepts for solving real life problems
  • CO4: Able to carry out implementation of algorithms using different simulation tools

CO – PO Mapping

CO1 3 3 3 3 3 3 3 3
CO2 3 3 3 3 3 3 2 3 3 3
CO3 3 3 3 3 3 3 2 3 3 3
CO4 3 3 2 3 2 3 3 3

DISCLAIMER: The appearance of external links on this web site does not constitute endorsement by the School of Biotechnology/Amrita Vishwa Vidyapeetham or the information, products or services contained therein. For other than authorized activities, the Amrita Vishwa Vidyapeetham does not exercise any editorial control over the information you may find at these locations. These links are provided consistent with the stated purpose of this web site.

Admissions Apply Now