In May 2022, a friendly person from Linux Magazine wrote to me and asked if I could write a short article on machine learning. After a few quick queries, I agreed, as you can see here. I'm going to try to be more creative in fulfilling this request than just restating the Wikipedia entry on machine learning techniques.

Now that we have known each other for a few lines, let me ask you a personal question: Did you have a friendly person from Linux Magazine in your mind's eye while you were reading the opening lines? Second question: No matter who it was – can you imagine why? Most of us piece images together in our heads from the experience we gain while reading. Because you didn't have any information, your brain used the experience it had, or thought it had, in combination with your knowledge of media in general or Linux Magazine to be more specific.

In other words, you used experience to make inferences about a new, unknown situation, and you do it all the time without even noticing. Of course, this doesn't always work, but – as often as not – that isn't a problem. Machine learning endeavors to give computers the ability to learn and generalize from experience. Experience, in computer-speak, means data, and the learning we're talking about has nothing to do with consciousness or intelligence in the strict sense. It's about acquiring and, if necessary, improving skills from experience without being programmed to do so using a legacy approach.

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