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Introduction to Machine Learning: Algorithms and Applications

Machine learning is a field of computer science that uses statistical techniques to enable computers to learn with data, without being explicitly programmed. Examples include optical character recognition, face detection, spam filtering, and more. The primary goal is to develop general-purpose algorithms that are practically valuable. Machine learning models need to be rich enough to capture real problems yet simple enough for mathematical study. Concept learning tasks involve searching through a space of hypotheses. Version space is the set of all valid hypotheses provided by an algorithm.

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Introduction to Machine Learning: Algorithms and Applications

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  1. 2017-2018

  2. IntroductiontoMachineLearning Itisalgorithmusedforlearningtodostuff isafieldofcomputersciencethatusesstatisticaltechniquestogivecomputer systemstheabilityto"learnwithdata,withoutbeingexplicitlyprogrammed ExampleofMachineLearning Opticalcharacterrecognition Facedetection Spamfiltering Topicspotting Spokenlanguageunderstanding Medicaldiagnosis Customersegmentation Frauddetection Weatherprediction GoalofMachineLearning Primarygoaldevelopgeneralpurposealgorithmofpracticalvalue Advantageofmachinelearningoverdirectprogramming MLResultmoreaccurate ML:humanexpertguidedbyimpreciseimpression ML:datadriven MachineLearningmodel Itisformallydefinelearningproblem Modelmustberichenoughtocaptureimportantaspectofreallearningproblem Modelmustbesimpleenoughtostudyproblemmathematically ConceptlearningTask Mostgeneralhypothesis(?) Mostspecifichypothesis(ф)

  3. Conceptlearningassearch CLsearchthroughalargespaceofhypothesisimplicitly

  4. VersionSpace Setofallvalidhypothesisprovidebyanalgorithm

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