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Notes on learning AI & ML

Hello Mati! I'm really really sorry I didn't respond to this sooner, I was really happy to see your message! To be honest I wasn't sure exactly the best way to introduce you to the concepts you wanted to learn about. Here's my best attempt.

Tom this is far too long just give me links

I know there's a lot here. I've summarised where I think you should start, and then I guess this page might be helpful to you as a reference.

Quick note before the list: ML & AI is mostly statistics. That can make it seem daunting, but most of the concepts can be explained very well visually, so youtube and interactive websites are the best way to learn, especially as a non-programmer (but that's how CS people learn this, too!). That makes citing hard in your situation. I'd recommend learning what actually matters to you — probably neural nets and some of the problems we try to solve with them, like clustering and classification — and some other high-level concepts, such as Optimisation, explainable AI, and supervised vs unsupervised learning. Once you know what matters to you, find some intro textbooks that cover what you care about, skim them to make sure you understand, and just cite those. Honestly, it's your best bet.

I'll send you any .pdfs you need (if you don't have access to anything I likely will through Glasgow Uni) and if I think of anything useful for you to cite academically, I'll let you know.

What do we mean by AI & ML?

The first thing we need to do is to work out what we actually mean by AI and ML. When you ask to learn about these things I think what you're getting at is along the lines of, "Computers are getting better and better at making 'decisions'. This has legal implications! How do they do that?" — and the answer isn't always AI or ML. And sometimes it's kind of AI or ML: they're both really just advanced statistics and clever observations. Is "stats" AI? Is "a program" ML? Where do we draw the line?

The truth is that there are more things that we use to make decisions than AI or ML, and some of them don't really have names.

The best way to approach this stuff is to learn some of the important concepts/tricks at a high level, and then go as deep into any particular approach using those tricks as you'd like. For example, one trick that's often used is "classification". Classification is our name for being shown an example of something and having to decide what "kind" of thing it is, labelling it. I have a student currently using this to take images of salmon and identify whether the salmon is a specific species that's currently causing problems in Scottish rivers. How do you classify / label it? There are all sorts of methods. But the problem comes up lots, and knowing that, you'll begin to realise that every AI / ML technique is really trying to solve only one problem out of a handful we care about. Mathematical concepts can also be useful: there are only a few that matter and everything else is really a special version of those. I'll list some below.

How should I learn them?

Don't read books or academic materials to learn from. They're useful to cite, but they're horrible for learning this stuff. Really.

Things you might want to learn

With that in mind, here's some things you might want to learn about.

AI & ML techniques

Traditional problems

Good things to cite

Much like somebody with a pretentious bookcase, I know these are important works in the philosophy of AI & ML, but I can't claim I've actually read them. Still, if they're useful to you, they'd make excellent citations in your dissertation.