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| − | ==What is Machine Learning==
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| − | Al tratar de encontrar una definición para ML me di cuanta de que muchos expertos coinciden en que no hay una definición standard para ML.
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| − | En este post se explica bien la definición de ML: https://machinelearningmastery.com/what-is-machine-learning/
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| − | Estos vídeos también son excelentes para entender what ML is:
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| − | : https://www.youtube.com/watch?v=f_uwKZIAeM0
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| − | : https://www.youtube.com/watch?v=ukzFI9rgwfU
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| − | Una de las definiciones más citadas es la definición de Tom Mitchell. This author provides in his book Machine Learning a definition in the opening line of the preface:
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| − | <blockquote>
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| − | {| style="color: black; background-color: white; width: 100%; padding: 0px 0px 0px 0px; border:1px solid #ddddff;"
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| − | <!--==============================================================================-->
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| − | <span style="color:#0000FF">
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| − | '''''Tom Mitchell'''''
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| − | </span>
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| − | <!--==============================================================================-->
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| − | <span style="color:#FFFFFF">
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| − | '''The field of machine learning is concerned with the question of how to construct computer programs that automatically improve with experience.'''
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| − | </span>
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| − | <!--==============================================================================-->
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| − | '''So, in short we can say that ML is about write''' <span style="background:#D8BFD8">'''computer programs that improve themselves'''</span>.
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| − | </blockquote>
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| − | Tom Mitchell also provides a more complex and formal definition:
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| − | <blockquote>
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| − | <!--==============================================================================-->
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| − | <span style="color:#0000FF">
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| − | '''''Tom Mitchell'''''
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| − | </span>
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| − | <!--==============================================================================-->
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| − | <!--==============================================================================-->
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| − | <span style="color:#FFFFFF">
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| − | '''A 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.'''
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| − | </span>
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| − | <!--==============================================================================-->
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| − | |}
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| − | Don't let the definition of terms scare you off, this is a very useful formalism. It could be used as a design tool to help us think clearly about:
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| − | :'''E:''' What data to collect.
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| − | :'''T:''' What decisions the software needs to make.
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| − | :'''P:''' How we will evaluate its results.
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| − | Suppose your email program watches which emails you do or do not mark as spam, and based on that learns how to better filter spam. In this case: https://www.coursera.org/lecture/machine-learning/what-is-machine-learning-Ujm7v
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| − | : '''E:''' Watching you label emails as spam or not spam.
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| − | : '''T:''' Classifying emails as spam or not spam.
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| − | : '''P:''' The number (or fraction) of emails correctly classified as spam/not spam.
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| − | </blockquote>
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| − | ===Types of Machine Learning===
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| − | ====Supervised Learning====
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| − | ====Unsupervised Learning====
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| − | ====Reinforcement Learning====
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