Introduction to Gaussian processes

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  1. Gaussian Process Summer School
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Комментарии
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I appreciate how thorough this video was. Many tutorials on GPs tend to handwave a lot of the mathematical details of Gaussians and use sloppy notation (which is a huge problem with machine learning education in general, in my opinion).

jonathancangelosi
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I really appreciate for this. Been studying GP with lots of confusion. This was light for me.

franard
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I loved learning that a diagonal noise term can help with ill-conditioned matrix inversions.

betube
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Thank you for these classes, very helpful - and probably COVID for enabling them to go to YouTube :-)

hossanatwino
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I don't see the slides on the website. the speaker says they are there ..

microndiamondjenkins
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thank you so much for this wonderful video. In most of your figures, you have about 10 different colors that are moving along the x-axis. On each slice (a vertical line at x=x_j lets say j is 5) we have 10 points in 10 different colors that are normally distributed while they are correlated based on a kernel with the 10 points in x_i {i=0, 1, .., 5, .., n}, and lets say n is 20. My question is how do you generate this point? In total we have 10 (colors) * 20 (n) = 200 points which have to satisfy 2 conditions: 1) being normally distributed at each section 2) following the correlation based on kernal. Thank you

mohsenvazirizade
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Any one who can say something about the kernel k(x, x'). Here what does it mean by x and x' I thought they are like two inputs of a random variables that gives a value, but I saw something like a vector k(x, x')=x^Tx'???. Does it mean that x is the observed points and x' is point for prediction?

muluegebreslasie