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This is a Twitter series on #FoundationsOfML. Today, I want to talk about another fundamental question:

โ“ What makes a metric useful for Machine Learning?

Let's take a look at some common evaluation metrics and their most important caveats... ๐Ÿ‘‡๐Ÿงต
Remember our purpose is to find some optimal program P for solving a task T, by maximizing a performance metric M using some experience E.

We've already discussed different modeling paradigms and different types of experiences.

๐Ÿ‘‰ But arguably, the most difficult design decision in any ML process is which evaluation metric(s) to use.
Read 25 tweets
This is a Twitter series on #FoundationsOfML.

โ“ Today, I want to start discussing the different types of Machine Learning flavors we can find.

This is a very high-level overview. In later threads, we'll dive deeper into each paradigm... ๐Ÿ‘‡๐Ÿงต
Last time we talked about how Machine Learning works.

Basically, it's about having some source of experience E for solving a given task T, that allows us to find a program P which is (hopefully) optimal w.r.t. some metric M.

According to the nature of that experience, we can define different formulations, or flavors, of the learning process.

A useful distinction is whether we have an explicit goal or desired output, which gives rise to the definitions of 1๏ธโƒฃ Supervised and 2๏ธโƒฃ Unsupervised Learning ๐Ÿ‘‡
Read 18 tweets
I'm starting a Twitter series on #FoundationsOfML. Today, I want to answer this simple question.

โ“ What is Machine Learning?

This is my preferred way of explaining it... ๐Ÿ‘‡๐Ÿงต
Machine Learning is a computational approach to problem-solving with four key ingredients:

1๏ธโƒฃ A task to solve T
2๏ธโƒฃ A performance metric M
3๏ธโƒฃ A computer program P
4๏ธโƒฃ A source of experience E
You have a Machine Learning solution when:

๐Ÿ”‘ The performance of program P at task T, as measured by M, improves with access to the experience E.

That's it.

Now let's unpack it with a simple example ๐Ÿ‘‡
Read 23 tweets

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