Gaussian Processes : Data Science Concepts

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

[@asjsingh]

My word! You are a fantastic communicator.

[@ritvikmath]

Really appreciate that!

[@undertaker7523]

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

[@ritvikmath]

Of course!

[@ghifariadamfaza3964]

Glad you cover this topic!

[@ritvikmath]

Hope you enjoyed it!

[@hesterklomp5266]

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

[@paull923]

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?

[@vzinko]

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

[@ritvikmath]

Hey, thanks!

[@ireoluwaTH]

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

[@ritvikmath]

Glad you liked it!

[@MecchaKakkoi]

Thanks for the great explanation!

[@ritvikmath]

Of course!

[@astrophage381]

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

[@vaibhavgupta2471]

Great explanation- really appreciate your effort in explaining this

[@ritvikmath]

Glad it was helpful!

[@davidheilbron]

Thank you so much

[@annamalaisriram7256]

Well explained , Timely need

[@ritvikmath]

Thanks, hope it helped!

[@GeoffryGifari]

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]

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]

@@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

[@marcfruchtman9473]

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

[@ritvikmath]

You and me both!

[@meanreversion1083]

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?

[@bin4ry_d3struct0r]

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

[@SleekGreek]

I love you.

[@user-oj9iz4vb4q]

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

[@ritvikmath]

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

[@user-oj9iz4vb4q]

@@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]

Poisson is the perfect distribution for fish

[@user-oj9iz4vb4q]

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

[@MrEo89]

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..