Supervised Learning – Georgia Tech – Machine Learning

This class is divided into
three subclasses, three parts. They are supervised learning.>>Yeah.
>>Unsupervised learning, and reinforcement. So, what do you think
supervised learning is?>>So, I think of supervised learning
as being the problem of taking labelled data sets, gleaning information from
it so that you can label new data sets.>>That’s fair. I call that function approximation. So, here’s an example
of supervised learning. I’m going to give you an input and
an output. And I’m going to give
them to you as pairs, and I want you to guess
what the function is.>>Sure.
>>Okay? Okay. 1, 1.>>Uh-huh. 2, 4.>>Wait, hang on,
is 1 the input and 1 the output,.>>Yes.
>>And 2 the input, and 4 the output?>>Correct.
>>All right. I’m on, I think I am on to you.>>3, 9.>>Okay.
>>4, 16.>>Nice.>>5, 25. 6, 36. 7, 49.>>Nice. This is a very hip data set.>>It is. What’s the function?
>>It’s hip to be squared.>>Exactly. Maybe. So if you believe that’s true, then tell me if the input is 10,
what’s the output?>>100.
>>And that’s right, if it turns out, in fact, that the function is x squared. But the truth is, we have no idea
whether this function is x squared. Not really.
>>I have a pretty good idea.>>You do?
>>Well->>Where’s that idea come from?>>It comes from having spoken with
you over a long period of time. And plus, you know, math.>>And plus math. Well, I’m going to->>You can’t say I’m wrong.>>You’re wrong.>>Oh.
>>Yeah, I did.>>You just said I was wrong.>>No, you’ve talked to me for
a long time, and plus math. I agree with that.>>Okay.
>>But I’m going to claim that you’re
making a leap of faith.>>Hm.
>>Despite being a scientist, by deciding that the input is 10 and
the output is 100.>>Sure.
I would agree with that.>>What’s that leap of faith?>>Well, I mean, from what you told me,
it’s still consistent with lots of other mappings from input to
output like 10 gets mapped to 11.>>Right or
everything is x squared except 10.>>Sure.>>Or everything is x,
x squared up to 10.>>Right, that would be mean->>That would be mean->>But it’s not logically impossible.>>What would be the median?>>A-ha.>>Thank you very much. I, I was saving that one up.

11 thoughts on “Supervised Learning – Georgia Tech – Machine Learning

  • I really liked the lectures helped me a lot with my data mining class. You appear very natural to me when you are joking, though the jokes are scripted (it's a guess). I really like the way you explain it, especially when each of you explain the same thing with different formulations.

  • We have got historical reports and we need to find the score (whether report is effective or useful or not) based on supervised learning. While doing the supervised learning process, we have to upload the words lists that are available in the reports. We have to assign the weightage for the word lists and run through all the reports to find the number of occurances. Now I am not able to understande how these word lists can be used to find the usefulness of the report?
    Based on your experience can you please explain how this word lists can be used as supervised data in finding the score of the report?

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