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Introduction to Machine Learning

Introduction to Machine Learning. Alejandro Ceccatto Instituto de Física Rosario CONICET-UNR. Bibliography. Machine Learning , Tom Mitchell ( McGraw Hill, 1997) Principal Component Analysis , Ian Jolliffe (Springer-Verlag, 2002)

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Introduction to Machine Learning

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  1. Introduction to Machine Learning Alejandro Ceccatto Instituto de Física Rosario CONICET-UNR 1er. Escuela Red ProTIC - Tandil, 18-28 de Abril, 2006

  2. Bibliography Machine Learning, Tom Mitchell (McGraw Hill, 1997) Principal Component Analysis, Ian Jolliffe(Springer-Verlag, 2002) An introduction to SVM and other kernel-based learning methods, Cristianini-Shawe Taylor (Cambrige, 2000) The Elements of Statistical Learning, Hastie-Tibshirani-Friedman (Springer, 2001) 1er. Escuela Red ProTIC - Tandil, 18-28 de Abril, 2006

  3. Machine Learning • The field of Machine Learning is concerned with the question of how to construct computer programs that automatically improve with experience • The purpose of this course is to present key algorithms and theory that form the core of Machine Learning 1er. Escuela Red ProTIC - Tandil, 18-28 de Abril, 2006

  4. Machine Learning • Interdisciplinary nature of the material: Statistics, Artificial Intelligence, Information Theory, etc. • Basic question: How to program computers to learn? 1er. Escuela Red ProTIC - Tandil, 18-28 de Abril, 2006

  5. Machine Learning Intelligent Data Analysis: • Intelligent application of data analytic tools (Statistics) • Application of “intelligent” data analytic tools (Machine Learning) Modern world: Data-driven world (industrial, commercial, financial, scientific activities) 1er. Escuela Red ProTIC - Tandil, 18-28 de Abril, 2006

  6. Why Machine Learning? • Recent progress in algorithms and theory • Growing flood of online data • Computational power available 1er. Escuela Red ProTIC - Tandil, 18-28 de Abril, 2006

  7. Why Machine Learning? • Niches for Machine Learning: • Data Mining: using historical data to improve decisions Medical records  medical knowledge • Software applications we can’t program by hand Autonomous driving Speech recognition • Self customizing programs Newsreader that learns user interests 1er. Escuela Red ProTIC - Tandil, 18-28 de Abril, 2006

  8. Why Machine Learning? • Data Mining • Data: Recorded facts • Information: Set of patterns, or expectations, that underlie the data • Data Mining: Extraction of implicit, previously unknown, and potentially useful information from data • Machine Learning: Provides the technical basis of data mining 1er. Escuela Red ProTIC - Tandil, 18-28 de Abril, 2006

  9. Why Machine Learning? • Typical Datamining Tasks • Risk of Emergency Cesarean Section Given • 9714 patient records, each describing a pregnancy and birth • Each patient record contains 215 features Learn to predict: • Classes of patients at high risk for emergency cesarean section 1er. Escuela Red ProTIC - Tandil, 18-28 de Abril, 2006

  10. Why Machine Learning? 1er. Escuela Red ProTIC - Tandil, 18-28 de Abril, 2006

  11. Why Machine Learning? One of the learned rules: IF No previous vaginal delivery, and Abnormal 2nd Trimester Ultrasound, and Malpresentation at admission THEN Probability of Emergency C-Section 0.6 Over training data: 16/41=0.63 Over Test Data: 12/20=0.60 1er. Escuela Red ProTIC - Tandil, 18-28 de Abril, 2006

  12. Why Machine Learning? • Credit Risk Analysis 1er. Escuela Red ProTIC - Tandil, 18-28 de Abril, 2006

  13. Why Machine Learning? • Customer Retention 1er. Escuela Red ProTIC - Tandil, 18-28 de Abril, 2006

  14. Why Machine Learning? • Problems Too Difficult to Program by Hand 1er. Escuela Red ProTIC - Tandil, 18-28 de Abril, 2006

  15. Why Machine Learning? • Software that Customizes to User 1er. Escuela Red ProTIC - Tandil, 18-28 de Abril, 2006

  16. Where is This Headed? Today: tip of the iceberg • First-generation algorithms: neural nets, decision trees, regression.... • Applied to well-formated databases Tomorrow: enormous impact • Learn across mixed-media data and multiple databases • Learn by active experimentation • Learn decisions rather than predictions • Cumulative, life-long learning 1er. Escuela Red ProTIC - Tandil, 18-28 de Abril, 2006

  17. Where is This Headed? Autonomous entities? “I'm sorry Dave; I can't let you do that.” –HAL 9000 in 2001: A Space Odyssey, by Arthur Clarke 1er. Escuela Red ProTIC - Tandil, 18-28 de Abril, 2006

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