L6introductionlearning

L6introductionlearning - Introduction to Supervised and...

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1 Introduction to Supervised and UnsuspervisedLearning Cse352 Lecture Notes Professor Anita Wasilewska Stony Brook University
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2 Learning Main Objectives Indentification of data as a source of useful information, called also a knowledge Use of ―learned‖ information (knowledge) for different applications
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3 Data – Information - Knowledge Data – as in databases Information , or knowledge is a meta information ABOUT the patterns hidden in the data The patterns must be discovered automatically
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4 Learning : Intuitive Definition • Learning is a process that extracts previously unknown knowledge from the data • It requires special algorithms, technologies and methods
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5 Learning There are many types of learning. We will cover two: SUPERVISED LEARNING : classification UNSUPERVISED LERANING : clustering The knowledge obtained in the learning process is often presented as a set of rules of the form: IF. ... THEN…. . It also finds other relationships in data
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Some Commercial Applications Market analysis and management • target marketing, customer relation management Risk analysis and management • Forecasting, customer retention, improved underwriting, quality control, competitive analysis
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7 More Applications • Buying patterns • Fraud detection • Customer Campaings • Decision support • Medical aplications • Marketing • and more
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Fraud Detection and Management (B1) Applications widely used in health care, retail, credit card services, telecommunications (phone card fraud), etc. Approach use historical data to build models of fraudulent behavior and use learned knowledge to help identify similar instances
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Fraud Detection and Management (B2) • Examples auto insurance : learn characteristics of group of people who stage accidents to collect on insurance and use them automatically to prevent fraud money laundering : learn characteristics of suspicious money transactions (US Treasury's Financial Crimes Enforcement Network) medical insurance : learn characteristics of fraudulent patients and doctors
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Fraud Detection and Management (B3) Detecting telephone fraud Use learning methods to describe telephone call model: destination of the call, duration, time of day or week. Detects patterns that deviate from an expected norm. British Telecom identified discrete groups of callers with frequent intra-group calls, especially mobile phones, and broke a multimillion dollar fraud. Detecting Credit Card fraud
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This note was uploaded on 01/25/2012 for the course CSE 352 taught by Professor Wasilewska,a during the Fall '08 term at SUNY Stony Brook.

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L6introductionlearning - Introduction to Supervised and...

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