PR-2018

Pattern Recognition (BTech, CSE & IT, 2018)

Course Objective:

The objective of the course is

• Get acquainted with the design and construction of a pattern recognition system

• Exposure to major approaches available in pattern recognition

• To get the idea of theoretical issues associated while designing a pattern recognition systems

• Hands on experience by implementing some popular pattern recognition techniques

Course Learning Outcomes:

Upon successful completion of the course, students will able to

• Explain and compare a variety of pattern classification, structural pattern recognition, and pattern classifier combination techniques

• Apply pattern recognition techniques to real-world problems such as image recognition, image classification, document analysis etc 

• Implement simple pattern classifiers, classifier combinations, and structural pattern recognizers etc

Syllabus:

          You can find the syllabus [ here ]. 

Books:

Text Book

T1.  Richard O. Duda, Peter E. Hart and David G. Stork, "Pattern Classification", John Wiley & Sons, 2001.

Reference Books

R1. Theodoridis, “Pattern Recognition”, 4th Edition, Elsevier, 2014.

R2. Christopher M. Bishop, “Pattern Recognition and Machine Learning”, 1st Edition, Springer, 2006. (Corr. 2nd printing 2011 edition

R3. V Susheela Devi and M Narasimha Murty, “Pattern Recognition: An Introduction”, 1st Edition, Universities Press, 2011

Evaluation:

1.       Quizzes: 15%

2.       Mid Term: 30%

3.       End Term Exam: 50%

4.    Teacher’s Assessment: 5%

Course Progress and Resources: [ *Reading list refers to the text book T1  ] 

• Some fundamental Research Articles:

  • Statistical Pattern Recognition: A Review, suggested by: A K Dash,  [ Link ]

  • Efficient Feature Selection via Analysis of Relevance and Redundancy, suggested by: Dr. S Vipsita, [ Link ]

  • A fuzzy K-nearest neighbor algorithm, suggested by: Dr. S Vipsita, [ Link ]

  • Feature selection for classification, suggested by: Dr. S Vipsita, [ Link ]

  • Tutorial on Maximum likelihood estimation, suggested by: A K Dash [ Link ]

• Course Progress: