Machine Learning Introduction

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Contents

Basic

What is machine learning: Develop learning algorithm. Using training set, machine finds the hypothesis that can predict target out of features.

Basic terms:

  • x(i): feature, or input
  • y(i): target, or output
  • (x(i), y(i)): training example
  • m: number of training example
  • data set of training example: training set
  • i: index into the training set
  • h: hypothesis
  • theta: parameter, or weight

Type of problem:

  • regression problem: target variable to predict is continuous
  • classification problem: target can take on a number of discrete values

Learning style:

  • supervised learning
  • unsupervisied learning
  • semi-supervised learning

Regression (statistical machine learning) algorithms:

  • linear regression
  • ordinary least squares regression (OLSR)
  • logistic regression
  • stepwise regression
  • multivariate adaptive regression splines (MARS)
  • locally estimated scatterplot smoothing (LOESS)


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