Відео

948 Benny Glen

Young Carol Harris Ruth Clark Jessica

Thank you for your explanation. I also think at 8:15 the multivariate normal distribution's probability density function should have $\sqrt{|\Sigma|}$ in the denominator (rather than $|\Sigma|$ as you have currently) and it also may be helpful to viewers to let them know that $p$ represents the dimension of the space we are considering

Thank you so much! 🙏🙏👍👍❤️❤️Are you able to provide slides for your videos, Prof Basener?

Super clear and simple. Thanks!

This beats my MIT lecture. WIll be coming back for more!

can you share these slides in the videos with me?

good explanation, funny that whenever he received an email notification I go check my inbox ='')

V não sei o meu não está o nome do meu amigo do cartão da minha irmã não tem nada a fazer com o 3f o meu não está funcionando não 3

I enjoyed watching your video, thank you. I will watch more of your videos on machine learning videos thank you!

Here is the link to the download site: apps.nationalmap.gov/lidar-explorer/#/

Sir where can i find yololidarTool.py can u provide that file

Thank you for sharing this. RF and SVM are the way to go with point clouds.

❤❤

Very great video! Thank you professor!! :)

Thank you

How do you get the values of 0.15 and 0.02? I'm getting different values.

A very good and concise explanation, even starting with the explanation of likelihood. Very well done!

Why do the stepwise functions have diagonals (slope != 0) joining the parts? shouldn't they all be joined by vertical lines, since they are continuous and yield either a 0 or a constant value?

The NFL is changing Bill! Let's up the weight on rushing yards (...I'll admit I'm a Baltimore fan).

Perfect

Excellent

10:48 ohhhhh, I was just going back and forth between the sections on LDA and QDA in three different textbooks (An Introduction to Statistical Learning, Applied Predictive Analytics, and Elements of Statistical Learning) for well over an hour and that multivariate normal pdf was really throwing me off big time. Mostly because of the capital sigma to the negative 1st power term, I didn't realize it was literally a capital sigma, I kept thinking it was a summation of something!

Good job. It is very easy to follow and understand

i was trying to read it my self but you made it so much simpler

good explanation, i hope there is always example of implementation

yoooo. This really helped me, my guy. Good work.

Thanks professore

Very useful information, thanks you professor!

13:42 correction: higher p-values indicate not very good predictors (insignificant); low predictors with p-values, actually, are good

9:15 you say we should expect 51% since up/(up+down) days equals 51%, but we should expect 50% accuracy with randomly guessing (via frequentist inference); 51% does not represent the number of time you correctly call the market up AND the number of time you correctly call the market down, which is what the Confusion Matrix does. So, (up days / (up days + down days)) does not represent accuracy; confusion matrix represents accuracy when up==up and down==down over total number of days. So confusion matrix is not the same as your 51%; cannot compare 52% with 51%.

thanks, can you do a video on neural networks from ISLR textbook?

So basically, ridge/lasso regression penalize for the *size* of the coefficients while aic/bic subset selection penalizes for the *number* of coefficients

This is an excellent series. Thank you so much for taking the time to make these

very good video, thank you professor

Thank you sir, well explained.

Blender!!! Shocked and Surprised !! Awesome 👍👍👍

You are so great. Keep up please.

could you share the slide？

Interesting and clear explanation! Thank you very much, this will help me in writing my thesis!

Thank you for sharing this, and thumbs up for visualization in Blender :)

Hi! If the classes are assumed to be normally distributed, does that subsume that the features making up an observations are normally distributed as well?

Thankyou so much ! Cleared a lot of my doubts

Thanks for this! I needed to clarify these methods in particular, was reading about them in ISLR

THANK YOU SO MUCH!!

Great video. Many thanks

How to classify LiDAR point cloud using machine learning in R.

How to classify LiDAR point cloud using machine learning in R

How to classify LiDAR point cloud using machine learning in R.

Great video. I love crunching numbers and building reports like this. I'm definitely not anywhere close to being on your level considering you're a professor, but I've always felt like I should be in a similar field just based on my interests and skillset. How cool would it be to be a sports data analyst?