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Pattern Recognition and Machine

LearningDr Suresh Sundaram

sureshsundaram@iitg.ernet.in

mailto:sureshsundaram@iitg.ernet.inmailto:sureshsundaram@iitg.ernet.in

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Pre requisites

Strong foundations in linear algebra,probability and optimization.

You are warned that if you lack these basics,you ll have a tough time battling EE 657!

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Reference books Pattern Classification : - Duda, Hart, Stork

Pattern recognition and Machine Learning :-Christopher Bishop

Neural networks for Pattern Recognition :-

Christopher Bishop

Introduction to Machine Learning :- Alpaydin

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Reference Books

Machine Learning :- Tom Mitchell

Pattern Recognition :- Sergios Theodoridis

Machine learning : a probabilistic perspective

:- Kevin Murphy

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Journals

IEEE TPAMI Pattern Recognition Pattern Recognition Letters Pattern Analysis and Applications IEEE TIP

IEEE Multimedia Speech Technology

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Conference

ICPR ICVGIP

ICASSP NIPS ICML ECCV ACCV.

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Grading

Two to three quizzes :- 25 marks Mid Term :- 30 marks

End Term :- 35 marks Assignments :- 10 marks

Zero TOLERANCE to copying !

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Course Material A fine blend of black board work with slide shows

The slides will be uploaded as soon as a class iscompleted

The slides may only give a glimpse of the courselecture for better understanding , you are suggestedto strongly read the appropriate sections of the

prescribed books.

Register on moodle Log in PRML2015

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Lets get started

Person identification systems -> Biometrics,Aadhar,

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Human Perception

How did we learn the alphabet of the Englishlanguage?

Trained ourselves to recognize alphabets, sothat given a new alphabet, we use ourmemory / intelligence in recognizing it.

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How about providing such capabilities tomachines to recognize alphabets ?

The field of pattern recognition exactly doesthat.

Machine Perception

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A basic PR framework

Training samples Testing samples

An algorithm for recognizing an unknown testsample

Samples are labeled (supervised learning)

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Typical supervised PR problem

Alphabets 26 in number (upper case)

# of alphabets/ classes to recognize 26. Collect samples of each of the 26 alphabets

and train using an algorithm.

Once trained, test system using unknown testsample/ alphabeth.

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Basics

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Handwriting Recognition

Input handwritten documentMachine print document

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Handwriting recognition

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Face recognition

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Fingerprint recognition

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Other Applications Object classification Signature verification ( genuine vs forgery) Iris recognition Writer adaptation Speaker recognition Bioinformatics (gene classification) Communication System Design

Medical Image processing

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Pattern Recognition Algorithms

Bag of algorithms that can used to providesome intelligence to a machine.

These algorithms have a solid probabilisticframework.

Algorithms work on certain characteristicsdefining a class - refered as features.

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Presence of a dot in i can distinguish thesei from l and is a feature.

Features values can be discrete or continuousin nature (floating value).

In practice, a single feature may not suffice fordiscrimination.