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Course Co-ordinated by IIT Madras
Coordinators
 
Prof. Sukhendu Das
IIT Madras

 
Prof. C.A. Murthy
Indian Statistical Institute

 

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Introduction and mathematical preliminaries - What is pattern recognition?, Clustering vs. Classification; Applications; Linear Algebra, vector spaces, probability theory, estimation techniques.

Classification: Bayes decision rule, Error probability, Error rate, Minimum distance classifier, Mahalanobis distance; K-NN Classifier, Linear discriminant functions and Non-linear decision boundaries.

Fisher’s LDA, Single and Multilayer perceptron, training set and test sets, standardization and normalization.

Clustering: Different distance functions and similarity measures, Minimum within cluster distance criterion, K-means clustering, single linkage and complete linkage clustering, MST, medoids, DBSCAN, Visualization of datasets, existence of unique clusters or no clusters.

Feature selection: Problem statement and Uses, Probabilistic separability based criterion functions, interclass distance based criterion functions, Branch and bound algorithm, sequential forward/backward selection algorithms, (l,r) algorithm.

Feature Extraction: PCA, Kernel PCA.

Recent advances in PR: Structural PR, SVMs, FCM, Soft-computing and Neuro-fuzzy.

 


Module.No

Modules / Topics

No.of Hours

Professor’s Name

1

Introduction and mathematical preliminaries

 

 

What is Pattern recognition; Applications and Examples

1

Prof.C.A.Murthy

Clustering vs. Classification; Supervised vs. unsupervised

1

Prof.Sukhendu Das

Relevant basics of Linear Algebra, vector spaces

2

Prof.Sukhendu Das

Probability Theory basics

1

Prof.C.A.Murthy

Basics of Estimation theory

1

Prof.C.A.Murthy

Decision Boundaries, Decision region / Metric spaces/ distances

1

Prof.C.A.Murthy

Mathematical Assignments

2 slides

 

2

Classification

 

 

Bayes decision rule, Error probability

2

Prof.C.A.Murthy

Examples

1

Prof.C.A.Murthy

Normal Distribution

1

Prof.Sukhendu Das

Linear Discriminant Function (equal covariance matrices)

1

Prof.Sukhendu Das

Non-linear Decision Boundaries (unequal covariance matrices)

2

Prof.Sukhendu Das

Mahalanobis Distance

1

Prof.Sukhendu Das

K-NN Classifier

1

Prof.Sukhendu Das

Fisher’s LDA

1

Prof.Sukhendu Das

Single Layer Perceptron

2

Prof.Sukhendu Das

Multi-layer Perceptron

2

Prof.Sukhendu Das

Training set, test set; standardization
and normalization

1

Prof.C.A.Murthy

List of Assignments

2-3 slides

 

3

Clustering

 

 

Basics of Clustering; similarity / dissimilarity measures; clustering criteria.

1

Prof.C.A.Murthy

Different distance functions and similarity measures

1

Prof.C.A.Murthy

Minimum within cluster distance criterion

1

Prof.Sukhendu Das

K-means algorithm;

1

Prof.Sukhendu Das

Single linkage and complete linkage algorithms, MST

3

Prof.C.A.Murthy

K-medoids, DBSCAN

1

Prof.C.A.Murthy

Data sets - Visualization; Unique Clustering; No existence of clusters

1

Prof.C.A.Murthy

Assignments

2 slides

 

4

Feature selection

 

 

Problem statement and Uses; Algorithms - Branch and bound algorithm, sequential forward / backward selection algorithms, (l,r) algorithm;

4

Prof.C.A.Murthy

Probabilistic separability based criterion functions, interclass distance based criterion functions

2

Prof.C.A.Murthy

5

Feature Extraction

 

 

PCA + Kernel PCA

3

Prof.Sukhendu Das

6

Recent advances in Pattern Recognition

 

 

Structural PR, SVMs, FCM, Soft-computing and Neuro-fuzzy techniques, and real-life examples

2

Prof.Sukhendu Das,            Prof.C.A.Murthy

 

Total

42

 

 

 

Vector spaces and Linear Algebra; Algorithms.

Probability theory; Statistics.


  1. R.O.Duda, P.E.Hart and D.G.Stork, Pattern Classification, John Wiley, 2001.

  2. Statistical pattern Recognition; K. Fukunaga; Academic Press, 2000.

  3. S.Theodoridis and K.Koutroumbas, Pattern Recognition, 4th Ed., Academic Press, 2009.


  1. http://www.ph.tn.tudelft.nl/PRInfo/

  2. http://kdd.ics.uci.edu/

  3. http://morden.csee.usf.edu/nnc/index1.html

  4. http://www.iapr.org/


C.M.Bishop, Pattern Recognition and Machine Learning, Springer, 2006.



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