4 Related Fields Introduction Artificial Intelligence Statistics Data Mining
5 Machine Learning Humour What is the difference between statistics, machine learning, AI and data mining?If there are up to 3 variables, it is statistics.If the problem is NP-complete, it is machine learning.If the problem is PSPACE-complete, it is AI.If you don't know what is PSPACE-complete, it is data mining.Source –
6 What is Machine Learning? Machines DO Machines LEARNShift in paradigm!Machines can be made to learn!How and for what purpose?How? By writing algorithms!Purpose: Mainly to Predict and to take Decisions!
7 Types of Learning Supervised Unsupervised Semi-supervised ReinforcementActiveDeep
8 Introduction Zoologists study learning in animals Psychologists study learning in humansIn this course, we focus on“Learning in Machines”Course ObjectiveStudy of approaches and algorithms that can make a machine learn
9 Introduction Machine Learning Subarea of AI that is concerned with algorithms/programs that can make a machine learnImprove automatically with experienceFor example- doctors learning from experienceImagine computers learning from medical records and suggesting treatment (automated diagnosis & prescription)
10 Machine LearningA computer program is said to learn from experience E with respect to some class of tasks T and performance measure P, if its performance at tasks in T, as measured by P, improves with experience E.
11 Interesting Problems Speech and Hand Writing Recognition Robotics (training moving robots)Search Engine (context aware)Learning to drive autonomous vehicleMedical DiagnosisDetecting credit card fraudComputational BioinformaticsGame Playing
12 What is Machine Learning? To solve a problem, we need an algorithm!For example: sorting a list of numbersInput: list of numbersOutput: sorted list of numbersFor some tasks, like filtering spam mailsInput: anOutput: Y/NWe do not know how to transform Input to OutputDefinition of Spam changes with time and from one individual to individualWhat to DO?Reference: E Alpaydin’s Machine Learning Book, 2010 (MIT Press)
13 What is Machine Learning? Collect lots of s (both genuine and spam)“Learn” what constitutes a spam mail (or for that matter a genuine mail)Learn from DATA!!For many similar problems, we may not have algorithm(s), but we do have example data (called Training Data)Ability to process training data has been made possible by advances in computer technologyReference: E Alpaydin’s Machine Learning Book, 2010 (MIT Press)
14 What is Machine Learning? Face Recognition!!!We humans are so good at it!!!Ever thought how we do it, despiteDifferent light conditions, pose, hair style, make up, glasses, ageing etc..Since we do not know how we do it, we can not write a program to do itML is about making inference from a sampleReference: E Alpaydin’s Machine Learning Book, 2010 (MIT Press)
15 Machine Learning Applications What kind of data I would require for learning?Credit card transactionsFace RecognitionSpam filterHandwriting/Character Recognition
16 Handwriting Recognition Task Trecognizing and classifying handwritten words within imagesPerformance measure Ppercent of words correctly classifiedTraining experience Ea database of handwritten words with given classifications
18 Pattern Recognition Example Handwriting Digit RecognitionReference: Christopher M Bishop: Pattern Recognition & Machine Leaning, 2006 Springer
19 Pattern Recognition Example Handwriting Digit RecognitionNon-trivial problem due to variability in handwritingWhat about using handcrafted rules or heuristics for distinguishing the digits based on shapes of strokes?Not such a good idea!!Proliferation of rulesExceptions of rules and so on…Adopt a ML approach!!Reference: Christopher M Bishop: Pattern Recognition & Machine Leaning, 2006 Springer
20 Pattern Recognition Example Handwriting Digit RecognitionEach digit represented by a 28x28 pixel imageCan be represented by a vector of 784 real no.sObjective: to have an algorithm that will take such a vector as input and identify the digit it is representingTake images of a large no. of digits (N) – training setUse training set to tune the parameters of an adaptive modelEach digit in the training set has been identified by a target vector t, which represents the identity of the corresp. digit.Result of running a ML algo. can expressed as a fn. y(x) which takes input a new digit x and outputs a vector y. Vector y is encoded in the same way as tThe form of y(x) is determined through the learning (training) phaseReference: Christopher M Bishop: Pattern Recognition & Machine Leaning, 2006 Springer
21 Pattern Recognition Example GeneralizationThe ability to categorize correctly new examples that differ from those in trainingGeneralization is a central goal in pattern recognitionPreprocessingInput variables are preprocessed to transform them into some new space of variables where it is hoped that the problem will be easier to solve (see fig.)Images of digits are translated and scaled so that each digit is contained within a box of fixed size. This reduces variability.Preprocessing stage is referred to as feature extractionNew test data must be preprocessed using the same steps as training dataReference: Christopher M Bishop: Pattern Recognition & Machine Leaning, 2006 Springer
22 Linear Classifiers in High-Dimensional Spaces Constructed Feature 2Var1Var2Constructed Feature 1Find function (x) to map to a different spaceGo back
23 A word about Preprocessing!! Can also speed up computationsFor eg.: Face detection in a high resolution video streamFind useful features that are fast to compute and yet that also preserve useful discriminatory information enabling faces to be distinguished form non-facesAvg. value of image intensity in a rectangular sub-region can be evaluated extremely efficiently and a set of such features are very effective in fast face detectionSuch features are smaller in number than the number of pixels, it is referred to as a form of Dimensionality ReductionCare must be taken so that important information is not discarded during pre processingReference: Christopher M Bishop: Pattern Recognition & Machine Leaning, 2006 Springer
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