A Gentle Introduction to Applied Machine Learning as a Search Problem

Last Updated on September 28, 2020

Applied machine learning is challenging because the designing of a perfect learning system for a given problem is intractable.

There is no best training data or best algorithm for your problem, only the best that you can discover.

The application of machine learning is best thought of as search problem for the best mapping of inputs to outputs given the knowledge and resources available to you for a given project.

In this post, you will discover the conceptualization of applied machine learning as a search problem.

After reading this post, you will know:

• That applied machine learning is the problem of approximating an unknown underlying mapping function from inputs to outputs.
• That design decisions such as the choice of data and choice of algorithm narrow the scope of possible mapping functions that you may ultimately choose.
• That the conceptualization of machine learning as a search helps to rationalize the use of ensembles, the spot checking of algorithms and the understanding of what is happening when algorithms learn.

Let’s get started.

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