001pattern recognition and machine learning 1
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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.
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