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  1. 1. Introduction to Machine Learning Alejandro Ceccatto Instituto de Física Rosario CONICET-UNR
  2. 2. Bibliography <ul><li>Machine Learning , Tom Mitchell ( McGraw Hill, 1997) </li></ul><ul><li>Principal Component Analysis , Ian Jolliffe (Springer-Verlag, 2002) </li></ul><ul><li>An introduction to SVM and other kernel-based learning methods , Cristianini-Shawe Taylor (Cambrige, 2000) </li></ul><ul><li>The Elements of Statistical Learning , Hastie-Tibshirani-Friedman (Springer, 2001) </li></ul>
  3. 3. Machine Learning <ul><li>The field of Machine Learning is concerned with the question of how to construct computer programs that automatically improve with experience </li></ul><ul><li>The purpose of this course is to present key algorithms and theory that form the core of Machine Learning </li></ul>
  4. 4. Machine Learning <ul><li>Interdisciplinary nature of the material: </li></ul><ul><li>Statistics, Artificial Intelligence, Information Theory, etc. </li></ul><ul><li>Basic question: </li></ul><ul><li>How to program computers to learn? </li></ul>
  5. 5. Machine Learning <ul><li>Intelligent Data Analysis: </li></ul><ul><li>Intelligent application of data analytic tools (Statistics) </li></ul><ul><li>Application of “intelligent” data analytic tools (Machine Learning) </li></ul><ul><li>Modern world: Data-driven world (industrial, commercial, financial, scientific activities) </li></ul>
  6. 6. Why Machine Learning? <ul><li>Recent progress in algorithms and theory </li></ul><ul><li>Growing flood of online data </li></ul><ul><li>Computational power available </li></ul>
  7. 7. Why Machine Learning? <ul><li>Niches for Machine Learning: </li></ul><ul><ul><li>Data Mining: using historical data to improve decisions </li></ul></ul><ul><ul><ul><li>Medical records  medical knowledge </li></ul></ul></ul><ul><ul><li>Software applications we can’t program by hand </li></ul></ul><ul><ul><ul><li>Autonomous driving </li></ul></ul></ul><ul><ul><ul><li>Speech recognition </li></ul></ul></ul><ul><ul><li>Self customizing programs </li></ul></ul><ul><ul><ul><li>Newsreader that learns user interests </li></ul></ul></ul>
  8. 8. Why Machine Learning? <ul><li>Data Mining </li></ul><ul><ul><li>Data: Recorded facts </li></ul></ul><ul><ul><li>Information : Set of patterns, or expectations, that underlie the data </li></ul></ul><ul><ul><li>Data Mining: Extraction of implicit, previously unknown, and potentially useful information from data </li></ul></ul><ul><ul><li>Machine Learning: Provides the technical basis of data mining </li></ul></ul>
  9. 9. Why Machine Learning? <ul><li>Typical Datamining Tasks </li></ul><ul><ul><li>Risk of Emergency Cesarean Section </li></ul></ul><ul><ul><ul><li>Given </li></ul></ul></ul><ul><ul><ul><li>9714 patient records, each describing a pregnancy and birth </li></ul></ul></ul><ul><ul><ul><li>Each patient record contains 215 features </li></ul></ul></ul><ul><ul><ul><li>Learn to predict: </li></ul></ul></ul><ul><ul><ul><li>Classes of patients at high risk for emergency cesarean section </li></ul></ul></ul>
  10. 10. Why Machine Learning?
  11. 11. Why Machine Learning? <ul><ul><li>One of the learned rules: </li></ul></ul><ul><ul><ul><li>IF No previous vaginal delivery, and Abnormal 2nd Trimester Ultrasound, and Malpresentation at admission </li></ul></ul></ul><ul><ul><ul><li>THEN Probability of Emergency C-Section 0.6 </li></ul></ul></ul><ul><ul><li>Over training data: 16/41=0.63 </li></ul></ul><ul><ul><li>Over Test Data: 12/20=0.60 </li></ul></ul>
  12. 12. Why Machine Learning? <ul><ul><li>Credit Risk Analysis </li></ul></ul>
  13. 13. Why Machine Learning? <ul><ul><li>Customer Retention </li></ul></ul>
  14. 14. Why Machine Learning? <ul><ul><li>Problems Too Difficult to Program by Hand </li></ul></ul>
  15. 15. Why Machine Learning? <ul><ul><li>Software that Customizes to User </li></ul></ul>
  16. 16. Where is This Headed? <ul><ul><li>Today: tip of the iceberg </li></ul></ul><ul><ul><ul><li>First-generation algorithms: neural nets, decision trees, regression.... </li></ul></ul></ul><ul><ul><ul><li>Applied to well-formated databases </li></ul></ul></ul><ul><ul><li>Tomorrow: enormous impact </li></ul></ul><ul><ul><ul><li>Learn across mixed-media data and multiple databases </li></ul></ul></ul><ul><ul><ul><li>Learn by active experimentation </li></ul></ul></ul><ul><ul><ul><li>Learn decisions rather than predictions </li></ul></ul></ul><ul><ul><ul><li>Cumulative, life-long learning </li></ul></ul></ul>
  17. 17. Where is This Headed? <ul><ul><li>Autonomous entities? </li></ul></ul>“ I'm sorry Dave; I can't let you do that.” – HAL 9000 in 2001: A Space Odyssey, by Arthur Clarke

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