Gaussian Processes : Data Science Concepts

Рет қаралды 8,581

Күн бұрын

All about Gaussian Processes and how we can use them for regression.
RBF Kernel : • Radial Basis Function ...
0:00 The Motivation
4:21 The Math
20:39 Importance of the Kernel
21:21 Extensions
22:18 Bayesian Stats

Пікірлер: 34

@hesterklomp5266 Ай бұрын

This video couldn't come at a better time, I have a statistical learning exam next week. Thank you so much!!!

@vzinko Ай бұрын

Happy to see you back here making great videos as always!

@ritvikmath Ай бұрын

Hey, thanks!

@asjsingh Ай бұрын

My word! You are a fantastic communicator.

@ritvikmath Ай бұрын

Really appreciate that!

@paull923 Ай бұрын

thank you very much, you make even the hardest topics understandable and fun to watch! could you delve a little bit deeper the mathematical steps of marginalization and conditional probability that you are talking about between 15:00 and 18:00?

@undertaker7523 Ай бұрын

I remember asking for this a while back. Thank you!!!

@ritvikmath Ай бұрын

Of course!

@ghifariadamfaza3964 Ай бұрын

Glad you cover this topic!

@ritvikmath Ай бұрын

Hope you enjoyed it!

@ireoluwaTH Ай бұрын

Definitely interested in the math! Thank you for another remarkable exposition Ritvik...

@ritvikmath Ай бұрын

Glad you liked it!

@astrophage381 Ай бұрын

I'm a simple man. When Ritvik posts, I watch.

@vaibhavgupta2471 Ай бұрын

Great explanation- really appreciate your effort in explaining this

@ritvikmath Ай бұрын

Glad it was helpful!

@MecchaKakkoi Ай бұрын

Thanks for the great explanation!

@ritvikmath Ай бұрын

Of course!

@davidheilbron Ай бұрын

Thank you so much

@meanreversion1083 Ай бұрын

Thank you for the video. it was nicely explained. There are a lot of simplifications. Could you also talk about how best select sigma and l - is it all done empirically? also do you have any example of implementation?

@marcfruchtman9473 Ай бұрын

Thanks for this explanation. Ah, now if I can just convince the fish to swim in a normal distribution when I fish...

@ritvikmath Ай бұрын

You and me both!

@annamalaisriram7256 Ай бұрын

Well explained , Timely need

@ritvikmath Ай бұрын

Thanks, hope it helped!

@SleekGreek Ай бұрын

I love you.

@GeoffryGifari Ай бұрын

If we try to predict the mean for the unknown points in between the data we have, would the mean always follow a straight line (ex: 0:45 one straight line, 24:05 two lines between 3 data points)?

@ritvikmath Ай бұрын

Definitely not! That’s a great question; I drew the straight lines out of simplicity and if you work out the math, the straight line would imply a mean of 13.75 for x=30 but as we see on the second page we actually got a mean of 13.9 there. The shape of the means curve will likely be nonlinear and will depend on the kernel that you choose.

@GeoffryGifari Ай бұрын

@@ritvikmath ahh i see. so I can get something like polynomial interpolation of μ'(x) if I pick the right kernel? thinking about it, straight line for the mean makes sense if our known data vector is the only thing that matters, but to get something "curvier" it makes sense that the distribution at one point is affected by the points nearby

@bin4ry_d3struct0r Ай бұрын

I only took Intro to Stats, so I never learned about kernels or even conditional distributions. Nonetheless, this is very interesting!

@user-oj9iz4vb4q Ай бұрын

modelling an integer quantity which must be greater than zero and chooses gaussian over poisson.....tsk tsk.

@ritvikmath Ай бұрын

Haha fair point! That’s what I get for trying to use a too-simple example 😆

@user-oj9iz4vb4q Ай бұрын

@@ritvikmath Oh of course, but it wouldn't be youtube without the trolls. I felt I needed to truely be part of the community.

@johningham1880 Ай бұрын

Poisson is the perfect distribution for fish

@user-oj9iz4vb4q Ай бұрын

@@johningham1880 sounds like something so crazy only a Frenchman would say it.

@MrEo89 Ай бұрын

Man your channel would blow up spectacularly if you invested the time into learning how to make really nice visuals.. the whole poorly hand drawn example thing is really 2005 && screams laziness and/or amateur..