Pattern Recognition

Learning Outcomes: 
After learning the course the students should be able to
• Understands the fundamental pattern recognition and machine learning theories
• Able to design and implement certain important pattern recognition techniques
• Able to apply the pattern recognition theories to applications of interest.
• Distinguish supervised learning methods from the unsupervised ones.
• Able to apply supervised learning methods (model-based maximum likelihood, k-nearest neighbours) to the classifier design.
• Able to apply k-means clustering algorithm
Syllabus: 
Unit NoTopics
1

Introduction

Paradigms for pattern recognition, Statistical and Syntactic pattern recognition, Soft and Hard computing schemes for pattern recognition. Statistical Pattern Recognition- Patterns and classes, Supervised, Semi-supervised, and Unsupervised classification

2

Representation

Vector space representation of patterns and classes, patterns and classes as strings, Tree-based representations, Frequent itemsets for representing classes and clusters, Patterns and classes as logical formulas

3

Proximity Measures

Dissimilarity measures, metrics, similarity measures, Edit distance, Hausdorff metric between point sets, Kernel functions, Contextual and conceptual similarity between points

4

Dimensionality Reduction

Feature selection: Branch and bound, Sequential feature  selection, Feature extraction: Fisher's linear discriminant, Principal components as features; Nearest Neighbor Classifiers- Nearest neighbor classifier, Soft nearest neighbor classifiers, Efficient algorithms for nearest neighbor classification, K-nearest neighbor classifier, minimal distance classifier, condensed nearest neighbor classifier and its modifications

5

Bayes Classifier

Bayes classifier, naïve Bayes classifier, Belief net; Decision Trees- Axis-parallel and oblique decision trees, Learning decision trees, Information gain and Impurity measures

6

Linear Discriminant Functions

Characterization of the decision boundary, Weight vector and bias, Learning the discriminant function, Perceptrons; Support Vector Machines- Maximizing the margin, Training support vector machines, Kernel functions

7

Clustering

Clustering process, Clustering algorithms, Clustering large datasets

8

Combination of Classifiers

AdaBoost for classification, Combination of homogeneous classifiers, Schemes for combining classifiers

Text Books: 
Name : 
Pattern Recognition: An Introduction
Author: 
Susheela D evi
M. NarasimhaMurty
Publication: 
Universities Press, Hyderabad, 2011.
Reference Books: 
Name: 
Pattern Classification
Author: 
R. O. Duda, P. E. Hart
Publication: 
John Wiley and Sons, 2000.
Name: 
Pattern Recognition
Author: 
M. NarasimhaMurty
V. Susheela Devi
Publication: 
NPTEL Web Course, 2011
Syllabus PDF: 
AttachmentSize
PDF icon Sem 5 BDA-Pattern Recognition.pdf188.21 KB
branch: 
BDA
Course: 
2018
Stream: 
B.Tech