AI News, How to Learn a Machine Learning Algorithm

How to Learn a Machine Learning Algorithm

The question of how to learn a machine learning algorithm has come up a few times on the email list.

In this post I&#8217;ll share with you the strategy I have been using for years to learn and build up a structured description of an algorithm in a step-by-step manner that I can add to, refine and refer back to again and again.

Desperate descriptions means resources such as the original descriptions of the method in the primary sources as well as authoritative secondary interpretations made of original descriptions in review papers and books.

like this approach because it defends the need to telescope in on a specific case of the algorithm from many possible cases at each step of the description while also leaving the option open for the description of variations.

Having a summary of usage heuristics collected together in one place can mean the difference of getting a good enough result quickly and running sensitivity analysis on the algorithm for days or weeks.

Other examples include the standard experimental datasets used to test the algorithm, the general classes of problem to which the algorithm is suited, and known limitations that have been identified and described for the algorithm.

You can start with a blank document and list out the section headings for the types of descriptions you need of the algorithm, for example applied, implementation, or your own personal reference cheat sheet.

Some examples where I have found this strategy practically useful include: In this last case, I turned my catalog into a book of 45 nature inspired algorithms which I published in early 2011.

You learned that algorithm descriptions are broken and the answer to learning an algorithm effectively is to design an algorithm template that meets your needs and to fill in the template as you read and learn about the algorithm.

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