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Introduction to machine learning Juan López González University of Oviedo

Introduction to machine learning Juan López González University of Oviedo Inverted CERN School of Computing, 24-25 February 2014. General overview. Lecture 1 Machine learning Introduction Definition Problems Techniques. Lecture 2 ANN SOMs Definition Algorithm Simulation

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Introduction to machine learning Juan López González University of Oviedo

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  1. Introduction to machine learning Juan López González University of Oviedo Inverted CERN School of Computing, 24-25 February 2014

  2. General overview • Lecture 1 • Machine learning • Introduction • Definition • Problems • Techniques • Lecture 2 • ANN • SOMs • Definition • Algorithm • Simulation • SOM basedmodels

  3. LECTURE 1 Introduction to machine learning and data mining

  4. 1.1. Somedefinitions1.2. Machine learning vs Data mining1.3. Examples1.4. Essence of machine learning1.5. A learningpuzzle 1. Introduction

  5. 1.1 Somedefinitions • To learn • To use a set of observations to uncover an underlying process • To memorize • To commit to memory • Itdoesn’t mean to understand

  6. 1.2 Machine learning vs Data mining • Machine learning(Arthur Samuel) • Study, designand development of algorithmsthatgivecomputerscapability to learnwithoutbeingexplicitlyprogrammed. • Data mining • Extractknowledgeorunknownpatternsfrom data.

  7. 1.3 Examples • Creditapproval • Gender, age, salary, years in job, currentdebt… • Spam filtering • Subject, From… • Topicspotting • Categorizearticles • Weatherprediction • Wind, humidity, temperature…

  8. 1.4 Essenceof machine learning • A patternexists • Wecannot pin itdownmathematically • Wehave data onit

  9. 1.5 A learningpuzzle

  10. 2.1. Components2.2. Generalization and representation2.3. Types of learning 2. Definition

  11. 2.1 Components • Input (customerapplication) • Ouput(aprove/rejectcredit) • Ideal function(f: X ↦ Y) • Data: (a1,b1,..,n1), (a2,b2,..,n2) … (aN,bN,..,nN) (historical records) • Result: (y1), (y2) … (yN) (loan paidornotpaid) • Hypothesis(g: X ↦ Y)

  12. 2.1 Components

  13. 2.2 Generalization and representation • Generalization • Thealgorithm has to build a general model • Objective • Generalize from experience • Ability to performaccuratelyforunseenexamples • Representation • Resultsdependon input • Input dependsonrepresentation • Pre-processing?

  14. 2.3 Types of learning • Supervised • Input and output • Unsupervised • Only input • Reinforcement • Input, output and grade of output

  15. 3.1. Regression3.2. Classification3.3. Clustering3.4. Association rules 3. Problems

  16. 3.1 Regression • Statistical process for estimating the relationships among variables • Could be usedforprediction

  17. 3.2 Classification • Identify to which of a set of categories a new observation belongs • Supervisedlearning

  18. 3.3 Clustering • Grouping a set of objects in such a way that objects in the same group are more similar • Unsupervisedlearning

  19. 3.4 Associationrule • Discovering relations between variables in large databases • Basedon ‘strong rules’ • Ifordermatters -> Sequentialpatternmining frequent itemset lattice

  20. 4.1. Decisiontrees4.2. SVM4.3. Monte Carlo4.4. K-NN4.5. ANN 4. Techniques

  21. 4.1 Decisiontrees • Uses tree-like graph of decisions and possible consequences • Internalnode: attribute • Leaf: result

  22. 4.1 Decisiontrees • Resultshuman readable • Easilycombinedwithothertechniques • Possiblescenarios can be added • Expensive

  23. 4.2 SupportVector Machine (SVM) • Separates the graphical representation of the input points • Constructs a hyperplanewhich can be usedforclassification • Input spacetransformationhelps • Non-human readableresults

  24. 4.3 Monte Carlo • Obtain the distribution of an unknown probabilistic entity • Random sampling to obtain numerical results • Applications • Physics • Microelectronics • Geostatistics • Computationalbiology • Computergraphics • Games • …

  25. 4.4 K-Nearestneighbors (K-NN) • Classifies by getting the class of the K closest training examples in the feature space K=1 K=5

  26. 4.4 K-Nearestneighbors (K-NN) • Easy to implement • naiveversion • High dimensional data needsdimensionreduction • Largedatasetsmakeitcomputationalexpensive • Many k-NN algorithms try to reduce thenumber of distanceevaluationsperformed

  27. 4.5 Artificial neural networks (ANN) • Systems of interconnectedneuronsthat compute from inputs

  28. 4.5 Artificial neural networks (ANN)

  29. 4.5 Artificial neural networks (ANN) Example:

  30. 4.5 Artificial neural networks (ANN) • Perceptron • single-layer artificial network with one neuron • calculates the linear combination of its inputs and passes it through a threshold activation function • Equivalent to a linear discriminant

  31. 4.5 Artificial neural networks (ANN) • Perceptron • Equivalent to a linear discriminant

  32. 4.5 Artificial neural networks (ANN) • Learning • Learntheweights (and threshold) • Samples are presented • If output isincorrectadjusttheweights and thresholdtowardsdesired output • Ifthe output iscorrect, do nothing

  33. Q & A

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