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8.4.1-Learning-Overview

8.4.1-Learning-Overview - Machine Learning Srihari Learning...

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Machine Learning Srihari 1 Learning Graphical Models: Overview Sargur Srihari [email protected]
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Machine Learning Srihari Topics • Motivation Goals of Learning 1. Density Estimation 2. Specific Prediction Tasks 3. Knowledge Discovery Learning as Optimization Empirical Risk and Overfitting Discriminative vs Generative Training Learning Tasks Model Constraints Data Observability Taxonomy of Learning Tasks 2 Alice Debbie Bob Charles BN MN
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Machine Learning Srihari Motivation Usual starting point is a given graphical model – Structure and parameters are part of input Two approaches to task of acquiring a model 1. Knowledge Engineering Construct a network by hand with expert s help 2. Machine Learning Learn model from a set of instances
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Machine Learning Srihari Knowledge Engineering vs ML Knowledge Engineering Approach – Pick variables, pick structure, pick probabilities – Effort Simple ones require hours of effort, complex one: months – Significant testing of model by evaluating results of typical queries yield plausible answers Machine Learning Approach – Instances available from distribution we wish to model – Easier to get large data sets rather than human expertise
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Machine Learning Srihari Difficulties with Manual Construction In some domains: – Amount of knowledge required too large – No experts who have sufficient understanding – Cost: expert time is valuable Properties of distribution change from one site to another Change over time – Expert cannot redesign every few weeks Modeling mistakes have serious impact on quality of answers 5
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Machine Learning Srihari Advantage of ML approach We are in the Information Age – Easier to obtain even large amounts of data in electronic form than to obtain human expertise Example Data – Medical Diagnosis Patient records – Pedigree Analysis (Genetic Inheritance) Family trees for disease transmission – Image Segmentation Set of images segmented by a person 6
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Machine Learning Srihari Medical Diagnosis Task Collection of patient records – History: Age, sex, history, medical complications – Symptoms – Results of tests – Diagnosis – Treatment – Outcome Task: Use data to model distribution of patients – Pathologist diagnoses disease of lymph nodes (Pathfinder 1992) 7
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Machine Learning Srihari Set of Family trees Task: Learn distribution – Breast cancer, Blood Type Three types of CPDs: – Penetrance model: phenotype given genotype Probability of a phenotype (say patient s blood type, B ) given person’s genotype (ordered pair of parent s blood- type, A,B or O): P(B(c)|G(c)) c=person, p=father , m=mother – Transmission model: genotype passed to child How often a genotype (locus for disease or blood type)
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