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Lecture Notes On Machine Learning

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Gaussian processes - CS229: Machine Learning

Gaussian processes - CS229: Machine Learning

cs229.stanford.edu

1See course lecture notes on “Supervised Learning, Discriminative Algorithms.” 2See course lecture notes on “Regularization and Model Selection.” 3See course lecture notes on “Support Vector Machines.” 4See course lecture notes on “Factor Analysis.” 1

  Lecture, Notes, Machine, Learning, Lecture notes, Machine learning, Gaussian

CS229LectureNotes - CS229: Machine Learning

CS229LectureNotes - CS229: Machine Learning

cs229.stanford.edu

To describe the supervised learning problem slightly more formally, our goal is, given a training set, to learn a function h : X → Y so that h(x) is a “good” predictor for the corresponding value of y. For historical reasons, this function h is called a hypothesis. Seen pictorially, the process is therefore like this: Training set house.)

  Machine, Learning, Machine learning, Cs229

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