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What is the definition of machine learning? What are the advantages of machine learning?

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In his book Machine Learning: A Probabilistic Perspective (2012), Kevin P. Murphy defines machine learning as

a set of methods that can automatically detect patterns in data, and then use the uncovered patterns to predict future data, or to perform other kinds of decision making under uncertainty (such as planning how to collect more data!)

He divides machine learning into three categories

  • supervised learning (or predictive), where the goal is to learn a mapping from inputs $\boldsymbol{x}$ to outputs $y$, given a labeled set of input-output pairs
  • unsupervised learning (or descriptive), where the goal is to find "interesting patterns" in the data
  • reinforcement learning, which is useful for learning how to act or behave when given occasional reward or punishment signals

What are the advantages of machine learning?

It can potentially be used to (at least partially) automate tasks that involve data analysis that were previously performed only by humans (e.g. translation). However, machine learning cannot automate all tasks: for example, it cannot infer causal relations from the data (which often must be done by humans), unless you include causal inference as part of machine learning. See e.g. Is machine learning less useful for understanding causality, thus less interesting for social science?.

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