Lecture 2 | Motivation of Resampling | Machine Learning Resampling Methods by Dr. Ahmad Bazzi

About this tutorial:

Video duration: 1:14:18
Lecture 2 of this course entitled “Machine Learning Resampling Methods” will serve as a transition from the review of statistics we did in Lecture 1 to the world of resampling, bootstrapping and Monte Carlo. Along the way, we shall demonstrate some important examples on R studio. The outline is as follows:

00:00:00 Intro
00:00:34 CDF Properties
00:11:13 Point Statistics
00:19:32 Motivation of Resampling
00:29:10 The main problem
00:29:38 R coding: Exponential CDF
00:35:54 R coding: Uniform CDF
00:37:19 R coding: Normal CDF
00:40:33 R coding:…

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10 thoughts on “Lecture 2 | Motivation of Resampling | Machine Learning Resampling Methods by Dr. Ahmad Bazzi

    Robert Junkins

    (June 13, 2019 - 12:00 pm)

    This is a very fitting explanation for me, hwo has studied statistics, but some time really clarifies things.

    Ron Shepherd

    (June 13, 2019 - 12:00 pm)

    this just wish he taught me!!

    Ronald Knudsvig

    (June 13, 2019 - 12:00 pm)

    , can we sample once ? And just do it again ? Get it?

    Angel Park

    (June 13, 2019 - 12:00 pm)

    Seriously good thoughtful presentation.

    Shayne Marcella

    (June 13, 2019 - 12:00 pm)

    Yes for maximum likelihood


    (June 13, 2019 - 12:00 pm)

    Excellent professor 🙂

    ato stephen

    (June 13, 2019 - 12:00 pm)

    Amazing! Thanks Ahmad Bazzi

    Maximus Larissa

    (June 13, 2019 - 12:00 pm)

    1:02:52 you don’t mean the p norm as the sum of p^th powers of elements of a given vector then the sum of that to the power 1/p ? I think this is just an R studio function to compute a certain distribution ?

    Herbert Juliette

    (June 13, 2019 - 12:00 pm)

    You have made it this far, you're awesome, give yourself a high-five Ha !!

    Fatima Mamari Fernandez

    (June 13, 2019 - 12:00 pm)

    Keep up the good work 👏🏻

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