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

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:…

## 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

#### choccers1

(June 13, 2019 - 12:00 pm)

Excellent professor 🙂

#### ato stephen

(June 13, 2019 - 12:00 pm)

#### 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 👏🏻