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CS 7641: Machine Learning - Part 1 of 3

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    HI, I'm a Mac.
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    >> [LAUGH] No. >> All right, we'll start over.
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    Let's do this right.
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    Hi, I'm Charles Isbeil.
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    >> True.
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    >> I'm a professor at Georgia Tech.
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    >> True.
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    >> This is Michael Littman.
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    >> True. >> He is also a professor at Georgia Tech.
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    >> False.
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    >> No, that's actually true, while you're a professor at Brown,
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    you're also an adjunct professor here at Georgia Tech.
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    I'm a professional donut thief.
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    >> False.
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    >> That is correct I am not a professional.
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    Together, we are teaching the course Machine Learning.
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    >> True.
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    >> The lectures will only be available on prime number calendar days.
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    >> False.
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    >> The mini course is on supervised learning.
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    >> True.
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    >> Supervised learning itself was invented in the 1830s by Gauss.
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    >> False?
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    >> Actually, I don't really know.
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    It sounds like the sort of thing you'd invent but, probably not,
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    so let's go with that.
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    Okay, supervised learning is the problem of learning to map inputs to
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    predictions of true or false.
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    >> False.
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    >> Good one, it also includes other kinds of predictions such as regression,
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    where the output might be vectors or numbers.
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    >> True.
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    >> It's an important component of all sorts of technologies from stopping credit
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    card fraud.
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    >> True. >> To finding faces in camera images.
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    >> True. >> To making taste your breath mints.
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    >> False?
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    >> I have no idea, but that sounded ridiculous so let's say false.
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    To recognizing spoken language.
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    >> True.
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    >> Our goal here is the give you the skills that you need to recognize how to
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    apply these technologies.
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    >> True.
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    >> And for interpreting their output, so
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    that you can solve a range of data science problems.
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    >> True.
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    >> And for surviving a robot uprising.
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    >> False. >> No, that is absolutely true.
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    The most important thing you could get out of this course is learning how to
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    survive the upcoming robot uprising.
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    Okay, very good.
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    You got an accuracy of 85%.
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    >> True.
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    >> Wrong, though after you answer this question,
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    you will have an accuracy of 85%.
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    >> True.
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    >> That is correct, but false also would have been correct.
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    Congratulations.
Title:
CS 7641: Machine Learning - Part 1 of 3
Description:

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Video Language:
English
Team:
Udacity
Project:
UD675: Machine Learning 1 - Supervised Learning
Duration:
01:48

English subtitles

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