L1 - EE7750 MACHINERECOGNITIONOFPATTERNS Lecture 1:...

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EE7750 MACHINE RECOGNITION OF PATTERNS Lecture 1: Introduction
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Grading Policy and Textbooks Midterm: %30 Final: %35 Homework assignments: %20 Semester project: %15 Bishop, “Pattern Recognition and Machine Learning,” Springer, (1 st edition), 2007. Duda, Hart, and Stork, “Pattern Classification,” Wiley Interscience, (2 nd edition), 2000.
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Outline of the Course Mathematical basics Probability distributions Statistical pattern recognition Dimensionality reduction Clustering Neural networks Advanced techniques
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Outline of first lecture topic Introduction Steps of a typical classification task Feature vector Mathematical basics: Polynomial curve fitting Probability theory Decision theory
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Examples Handwriting recognition Face recognition Emotion recognition Target recognition Speech recognition
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Classification Example Goal : Automatically classify incoming fish according to species, and send to respective packing plants. Features : Length, width, color, brightness, etc. Model : Sea bass have some typical length, and it is greater than that for salmon.
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This note was uploaded on 09/21/2010 for the course EE EE7750 taught by Professor Bahadirgunturk during the Fall '10 term at LSU.

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L1 - EE7750 MACHINERECOGNITIONOFPATTERNS Lecture 1:...

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