ECS124_Lecture7

ECS124_Lecture7 - ECS 124 Theory and Practice of...

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ecture 7 : Introduction to Machine ECS 124 Theory and Practice of Bioinformatics Lecture 7 : Introduction to Machine Learning Instructor: Ilias Tagkopoulos [email protected] Office: Kemper 3063 and GBSF 5313 4/21/2010 1 UC Davis
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High-throughput measurement of gene expression: Microarrays 4/21/2010 UC Davis 2
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Measure the number of cells in a picture 4/21/2010 UC Davis 3 Goal: build a program that you input an image an it tells you the number of cells in it
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Basics of Pattern recognition c Two ways: classification and clustering c In classification there exists a training set (learning phase) where inputs are labeled; not in clustering c Generalization: The ability to categorize correctly 4/21/2010 UC Davis 4 new samples c Preprocessing and feature extraction : “Transform” the original input variables into some new space variables in order to reduce dimensionality , variability and speed-up computation
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xpression in Exp 2 Pattern recognition and matching: Clustering vs. classification
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This note was uploaded on 05/30/2010 for the course ECS ECS 124 taught by Professor Tagkopoulos during the Spring '10 term at UC Davis.

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ECS124_Lecture7 - ECS 124 Theory and Practice of...

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