in this article, we will try to define what it is and also try to give you a sense of when you want to use machine learning Even among machine learning practitioners there isn’t a well-accepted definition of what is and what isn’t machine learning.
But let me show you a couple of examples of the ways that people have tried to Define it Here’s the definition of what is machine learning that is due to author Samuel.
He defined machine learning as the field of study that gives computers the ability [to] learn without being explicitly programmed Samuels’s claim to fame was that back in the He wrote a checkers playing program and the amazing thing about this checkers playing program Was that [officer] Samuel [himself] wasn’t a very good checkers player?
but what he did was he had the program play tens of thousands of games against itself By watching what sorts of the board [positions] tended to lead to wins and what sort of board positions?
Tended to Veto losses the checkers playing program learned over time What are good board positions and what a bad board positions and eventually learned to play checkers better than Arthur Samuel himself was able [to] This was a remarkable result also Samuel [himself] turned out not to be a very good checkers player But because the computer has the patience to pay tens of thousands of games against itself No, human has the patience to play that many games By doing this the computer was able to get so much checkers playing experience that it eventually became a better checkers player than offered Samuel himself This is [somewhat] informal definition and an older one here’s a slightly more recent definition by Tom Mitchell Who is a friend out in Committee Mellon? So Tom defines machine learning by saying that a well-posed learning problem is defined as follows He says a computer program is said to learn from experience II concerning some [tosti] and some performance measure key Evidence Performance on t as measured by P Improves of experience II I actually think he came up with this definition just to make it wrong For the checkers playing examples the experience II would be the experience of having the program play tens of thousands of games against itself The task t would be the task of playing checkers and the performance measure p would be the probability That wins the next game of checkers against some new opponent Throughout these [videos] besides me trying to teach you to stuff I’ll occasionally ask you a question to make sure you understand the content. Here’s one on top of the definition of machine learning by Tom mcCullar Let’s say your email program watches which emails you do or do not [value] spam so in an email client like [this] you might click [this] spam button to report some email as spam but not other emails and Based on which emails you marcus them say your email program learns better how to filter a spam email what is the task of [t] in the setting in a few seconds the article will pause and when it does so you can use your mouse to select one [of] these four radio buttons to Let to let me know which of these four you think is the right answer to this question So hopefully you got that this is the right answer classifying emails is a toss t in fact this definition defines the task t a performance measure p and the some experience [E] and so watching you label emails as spam or non-sPam this would be the experience E and the [fraction] of emails correctly classified that might be our performance measure p and So our task performance on the [task] [about] systems performance on the task t on the performance measure p will improve after the experience e In this course I hope to teach you about various different types of learning algorithms there are several different types of learning algorithms the main two types are what we call supervised learning and unsupervised learning I’ll define what these terms mean more in the next couple articles But it turns out that in supervised learning the idea is we’re going to teach the how to do something Whereas in unsupervised learning We’re going to let it learn by itself don’t worry if these two terms.
Don’t make sense yet in the next two articles I’m going to say exactly what these two types of learning you will also hear all the buzz terms such as reinforcement learning and recommender systems These are other types of machine learning algorithms that we’ll talk about later but the two most used types of learning algorithms are probably supervised learning and Unsupervised learning and I’ll define them in the next two articles and we’ll spend most as far as talking about these two types of learning algorithms It turns out one of the other things we’ll spend a lot of time on in this class is practical advice for applying learning algorithms This is something [that] I feel pretty strongly about and exactly something that I don’t know of any other [university] teachers Teaching about learning algorithms. It’s like giving you a set of tools and Equally important or more important than giving you the tools is to teach you how to apply these tools I like to make an analogy to learning to become a carpenter Imagine that someone is teaching you how to be a carpenter, and they say here’s a hammer Here’s a drive screwdriver is a saw good luck well That’s no good right you have all these tools, but the more important thing is to learn how to use these tools properly There’s a huge difference We can keep between people that know how to use these machine learning algorithms versus people that don’t know how to use these tools well here in Silicon Valley when I live When I go visit different companies even at the top silicon Valley companies very often I see people trying to apply Machine learning Algorithms to some problem and sometimes there have been going at it for six months But sometimes when I look at what they’re [doing] I say you know, I could have told them like gee I could have told you six months ago or that you should be taking a learning algorithm, and applying it in like the slightly modified way and Your chance of success would have been much higher um So what we’re going to do in this class is [actually] spend a lot of time talking about how if you’re actually trying to develop a machine learning system How to make those best practices types decisions about the way in which you build your system, so that when you apply learning algorithm You’re less likely to end up one of those people who end up pursuing somehow for six months that you know Someone else could have figured out [just] wasn’t gonna work at all and it’s a waste of time for six months So I’m actually spend a lot of time teaching you Those sorts of best practices in Machine learning and AI and how to get [the] stuff to work And how we do it or how the best people do it in Silicon Valley and around the world I hope to make you one of the best people and knowing how to design and build serious machine learning and Ai systems So that’s machine learning and these are the main [topics].
I hope to teach in the next article. I’m going to Define What is supervised learning and after that what is unsupervised learning and also start to talk about when you will use each of them?
