Mrinal Kanti Bhowmik, Ph.D. (Engg.)
Department of Computer Science and Engineering
Tripura University (A Central University)

Subject Name: Pattern Recognition


Also covered: Machine Learning and Preliminaries of Deep Learning
Subject code: CSE 1001E1
Total course time: 40 hours
Credit- 04

Pre-Requisite: Linear Algebra, Probabilities and Statistics
Books of Linear Algebra:

  1. I. C. F. Ipsen, Numerical Matrix Analysis Linear systems and Least Squares. Society for Industrial and Applied Mathematics, 2009.

Books of Probabilities and Statistics:

  1. N.G. Das, “Statistical Methods”, Combined Edition (Volumes I & II), Tata McGraw Hill, 2009.
  2. Tianhu Lei, “Statistics of Medical Imaging”, CRC Press, Taylor & Francis Group, 2012.
  3. V. Sundarapandian, “Numerical Linear Algebra”, PHI, 2008.
  4. Erwin Kreyszig, “Advanced Engineering Mathematics”, 9th Edition, Wiley, 2006.
Mission: To educate students in the areas of Machine Learning and Pattern Recognition fields by providing best practices of teaching learning process for careers in higher education and research.
Vision: Pattern Recognition, Machine Learning, Computer Vision and Deep Learning to be used as transformative technologies for human welfare and knowledge economy, thereby helping the nation in achieving its sustainable developments.
Lectures and their Durations Covered Topics Class supports* Own created Lecture notes

L.1
Duration: 4 Hours
Introduction To Pattern Recognition MKB.L.1.pdf
MKB.L.1.ppt
L.1.1
Overview of patterns and its example CS.L.1.1.pdf MKB.L.1.1.pdf
MKB.L.1.1.ppt
L.1.2
Pattern Recognition and its Application CS.L.1.2.pdf MKB.L.1.2.pdf
MKB.L.1.2.ppt
L.1.3
Phases of Pattern Recognition (Design Cycle) CS.L.1.3-1.pdf
CS.L.1.3-2.pdf
MKB.L.1.3.pdf
MKB.L.1.3.ppt
L.1.4
Learning of machine and its types CS.L.1.4-1.pdf
CS.L.1.4-2.pdf
MKB.L.1.4.pdf
MKB.L.1.4.ppt
L.1.5
Different Approaches of Pattern Recognition CS.L.1.5-1.pdf
CS.L.1.5-2.pdf
CS.L.1.5-3.pdf
MKB.L.1.5.pdf
MKB.L.1.5.ppt
L.2
Duration: 14 Hours
Statistical Approach of Pattern Recognition MKB.L.2.pdf
MKB.L.2.ppt
L.2.1
Overview of Statistical Analysis CS.L.2.1.pdf MKB.L.2.1.pdf
MKB.L.2.1.ppt
L.2.2
Various approaches of Statistical Analysis MKB.L.2.2.pdf
MKB.L.2.2.ppt
L.2.2.1
LDA(Linear Discriminant Analysis) CS.L.2.2.1.pdf MKB.L.2.2.1.pdf
MKB.L.2.2.1.ppt
L.2.2.1.1
Introduction to LDA CS.L.2.2.1.1-1.pdf
CS.L.2.2.1.1-2.pdf
CS.L.2.2.1.1-3.pdf
MKB.L.2.2.1.1.pdf
MKB.L.2.2.1.1.ppt
L.2.2.1.2
Application of LDA MKB.L.2.2.1.2.pdf
MKB.L.2.2.1.2.ppt
L.2.2.1.3
Steps of LDA explain with examples MKB.L.2.2.1.3.pdf
MKB.L.2.2.1.3.ppt
L.2.2.2
PCA(Principal Component Analysis) MKB.L.2.2.2.pdf
MKB.L.2.2.2.ppt
L.2.2.2.1
Introduction to PCA CS.L.2.2.2.1.pdf MKB.L.2.2.2.1.pdf
MKB.L.2.2.2.1.ppt
L.2.2.2.2
Steps of PCA explain with example CS.L.2.2.2.2-1.pdf
CS.L.2.2.2.2-2.pdf
CS.L.2.2.2.2-3.pdf
CS.L.2.2.2.2-4.pdf
MKB.L.2.2.2.2.pdf
MKB.L.2.2.2.2.ppt
L.2.2.3
LDA vs PCA MKB.L.2.2.3.pdf
MKB.L.2.2.3.ppt
L.2.2.3
LDA vs PCA MKB.L.2.2.4.pdf
MKB.L.2.2.4.ppt
L.2.2.4
Pattern Classification using Statistical Approaches CS.L.2.2.4.pdf MKB.L.2.2.4.pdf
MKB.L.2.2.4.ppt
L.2.2.4.1
Nearest Neighbor Classifier CS.L.2.2.4.1.pdf MKB.L.2.2.4.1.pdf
MKB.L.2.2.4.1.ppt
L.2.2.4.2
K-Nearest Neighbor Classifier CS.L.2.2.4.2.pdf MKB.L.2.2.4.2.pdf
MKB.L.2.2.4.2.ppt
L.2.2.4.3
Modified K-Nearest Neighbor Classifier CS.L.2.2.4.3.pdf MKB.L.2.2.4.3.pdf
MKB.L.2.2.4.3.ppt
L.2.2.4.4
Fuzzy K-Nearest Neighbor Classifier CS.L.2.2.4.4.pdf MKB.L.2.2.4.4.pdf
MKB.L.2.2.4.4.ppt
L.3
Duration: 6 Hours
Syntactic Approach of Pattern Recognition MKB.L.3.pdf
MKB.L.3.ppt
L.3.1
Overview of Syntactic Approach CS.L.3.1.pdf MKB.L.3.1.pdf
MKB.L.3.1.ppt
L.3.2
Flowchart of Syntactic Approach CS.L.3.2.pdf MKB.L.3.2.pdf
MKB.L.3.2.ppt
L.3.3
Several Techniques in Syntactic approach CS.L.3.3.pdf MKB.L.3.3.pdf
MKB.L.3.3.ppt
L.3.3.1
Hopcroft-Karp algorithm CS.L.3.3.1-1.pdf
CS.L.3.3.1-2.pdf
CS.L.3.3.1-3.pdf
MKB.L.3.3.1.pdf
MKB.L.3.3.1.ppt
L.3.3.2
String Matching Algorithm CS.L.3.3.2-1.pdf
CS.L.3.3.2-2.pdf
CS.L.3.3.2-3.pdf
MKB.L.3.3.2.pdf
MKB.L.3.3.2.ppt
L.4
Duration: 8 Hours
Neural Network of Pattern Recognition MKB.L.4.pdf
MKB.L.4.ppt
L.4.1
Overview of Neural Network CS.L.4.1.pdf MKB.L.4.1.pdf
MKB.L.4.1.ppt
L.4.2
Inspiration from Neurobiology CS.L.4.2.pdf MKB.L.4.2.pdf
MKB.L.4.2.ppt
L.4.3
Neural Network Architectures CS.L.4.3.pdf MKB.L.4.3.pdf
MKB.L.4.3.ppt
L.4.3.1
Feed Forward Neural Network CS.L.4.3.1.pdf MKB.L.4.3.1.pdf
MKB.L.4.3.1.ppt
L.4.3.2
Activation Functions CS.L.4.3.2.pdf MKB.L.4.3.2.pdf
MKB.L.4.3.2.ppt
L.4.3.3
Common Activation Functions CS.L.4.3.3.pdf MKB.L.4.3.3.pdf
MKB.L.4.3.3.ppt
L.4.3.4
Applications of Feed Forward Neural Network CS.L.4.3.4.pdf MKB.L.4.3.4.pdf
MKB.L.4.3.4.ppt
L.4.3.5
Limitations of Perceptron MKB.L.4.3.5.pdf
MKB.L.4.3.5.ppt
L.4.3.6
Multi-layer Feed Forward Neural Network CS.L.4.3.6.pdf MKB.L.4.3.6.pdf
MKB.L.4.3.6.ppt
L.4.3.7
Applications of Multi-layer Feed Forward Neural Network
L.5
Duration: 8 Hours
Deep Learning CS.L.5.pdf MKB.L.5.pdf
MKB.L.5.ppt
L.5.1
History of Deep Learning and success Stories CS.L.5.1.pdf MKB.L.5.1.pdf
MKB.L.5.1.ppt
L.5.2
Introduction to Deep Learning CS.L.5.2.pdf MKB.L.5.2.pdf
MKB.L.5.2.ppt
L.5.3
Feed Forward Network CS.L.5.3.pdf MKB.L.5.3.pdf
MKB.L.5.3.ppt
L.5.4
Deep Neural Network CS.L.5.4.pdf MKB.L.5.4.pdf
MKB.L.5.4.ppt
L.5.5
Better training of neural networks CS.L.5.5.pdf MKB.L.5.5.pdf
MKB.L.5.5.ppt
L.5.6
Recurrent Neural Network CS.L.5.6.pdf MKB.L.5.6.pdf
MKB.L.5.6.ppt
L.5.7
Convolutional Neural Network CS.L.5.7.pdf MKB.L.5.7.pdf
MKB.L.5.7.ppt
L.5.8
Generative Models CS.L.5.8.pdf MKB.L.5.8.pdf
MKB.L.5.8.ppt
L.5.9
Recent Trends CS.L.5.9.pdf MKB.L.5.9.pdf
MKB.L.5.9.ppt
L.5.10
Applications CS.L.5.10.pdf MKB.L.5.10.pdf
MKB.L.5.10.ppt

Refereed Book:

  1. Pattern Classification: R.O.Duda , P.E.Hart and DG.Stork (John Wiley & Sons.Inc,UK)
  2. Pattern Recognition-Statistical, Structural and Neural Approaches: R. Schalkoff(John Wiley & Sons.Inc,UK)
  3. Pattern Recognition-An Introduction:M.N.Murty and V.S.Devi(Universities Press(India)Pvt.Ltd)




Today's visitors: 22

Total no of visitors: 14657

Copyright © 2020 | All Rights Reserved.

Contact
DR. Mrinal Kanti Bhowmik

mrinalkantibhowmik@tripurauniv.in
+91 9436129933
Admin Login