Lecture 15 - Kernel Methods

[@guptaachin]

This makes me add him to the category of artists like Prof Gilbert Strang. I wonder how do you develop such skill of so lucidly explaining the most intricate concepts?

[@clarkupdike6518]

Obviously one would have to know the material inside and out but also at both a theoretical and applied level. I'm guessing he has honed his technique through years of experience with students and is able to dial into that sweet spot of just enough, but not too much, detail--while providing lots of context and interlinking of related concepts. He only goes theoretical, and judiciously at that, when it adds insight... instead of grandstanding to show off. He is truly a master at bringing students along for the ride on complex subjects.

[@amoughnieh]

This man answered a lot of lingering questions I had, even after reading multiple articles, papers, and watching experts on KZbin.

[@sakules]

"if i went to enter the Z space, you would have never heard from me again" haha so great

[@chilling00000]

lecture 14,15,16 are the best SVM videos on youtube

[@saiprasad8311]

Very valuable addition to all ML text books, in which one can easily get drowned in the mathematics involved. He is superb to elicit the meaning of mathematics without going into the complexities of the same. Thanks for this course.

[@livnatje]

An amazing lecturer. His talks are perfectly clear, insightful and interesting. Thanks for putting this online!

[@alvincay100]

Just to reiterate what other commenters are saying.... simply excellent. I've found multiple sources and could not wrap my head around the kernel trick until I found these lectures. Abu-Mostafa separates the important concepts out from the mathematical details so that you can understand the important concepts at hand. It is easy to fill in the details later once you understand the important concept.

[@Kristofcoddens]

By far best the explanation on kernels in SVMs I found online.

[@vasjaforutube1]

Professor Abu-Mostafa is such a cheerful person. His explanation is very clear, but I still have to pause the video every once in a while just to have a laugh.

[@TheCpHaddock]

Sir you are one of the best professors ever! And not just in machine learning!

[@ahlammallak8853]

you are just amazing ^^ many thanks professor, I hope if you can add more videos for more techniques such as PCA, ICA and deep learning

[@gip8507]

These lectures are really great. Exceptionally clear and fun to watch. Thank you so much for this

[@zuodongzhou3334]

excellent lecture ! Best explanation I have ever seen.

[@etiennedoumazane7556]

I think you just gave me a bit of intuition of what that mysterious kernel trick is... thanks!

[@jakebruce1617]

This has been extremely helpful. Thanks for posting!

[@brainstormingsharing1309]

Absolutely well done and definitely keep it up!!! 👍👍👍👍👍

[@ShaymaaKhalifa]

This professor is brilliant!

[@acgalt]

Excellent lecture. Congratulations!

[@JulienAmelot]

Conclusion : What happens in the Z space stays in the Z space :P

[@markh1462]

Lol

[@kevinlin4157]

Thank you Professor Yaser, it clearly explain how Kernal reduce computation.

[@fengzhengkite]

You are excellent

[@5up5up]

im the happiest guy in the world, i finally understood what's the kernel freaking trick.. thank you! شكراً جدا لحضرتك

[@denoleyn]

Thank you for this great lecture. Everything explained veryvery clearly.

[@dergarten776]

excellent demonstration of kernel methods!

[@DAsiaView_]

Awesome lecture, had my interest the entire time!

[@nicktgr152]

Fantastic presentation. Thank you very much.

[@fierydino9402]

Thank you for the precious lectures!!

[@AndyLee-xq8wq]

Great explanation!

[@pt77780]

"... terms will be dropping like flies" lol

[@go2chayan]

I burst into laughs when he described positive-semidefinite matrix in terms of a "sleeping vector" and "standing vector" at 45:00

[@vman049]

18:17 blew my mind. LOL'ed at 26:30. All the elements of a great lecture! Excellent!

[@jandal487]

Excellent course on Introduction to ML. Thank you professor :)

[@Darshanhegde]

and again at 41:42 Prof. Yaser says: "Third way to check that kernel is valid is 'who cares' for mercer's theorem :)"

[@dipanjans]

Thanks a lot Prof. Yaser.

[@BreatheThePureSerene]

Brilliant teacher

[@sarnathk1946]

You are Pedhanna (Big brother) from now on! Thank you!

[@emademad4]

a question : he said an objective function of number of miss classification is NP hard , why ? and if it is so in soft margin SVM, the amount of violation need to be minimized and to perform this u need to check every sample whether they are violating or not, so its the same action he called np-hard. any one who knows where im wrong id be glad to hear it.

[@aztmln]

very useful lecture. Thanks Prof ! Would love to hear more

[@chaitanyatanwar8151]

Thanks Superb Lecture..

[@fatimatayeb677]

Great dude . Keep it up .

[@michaelmellinger2324]

@45:30 Establish that the Z-space exists even if we don’t know what it is

[@3198136]

Thank you so much, it's much better than the class I attended for Pattern Recognition!

[@mohamedsalem9806]

This is brilliant!

[@Darshanhegde]

It's hilarious :) Prof. Yaser at 31:30 says: " If I had gone to Z space (Which is infinite here), you would have never heard from me again :D "

[@DiegoAToala]

Great lecture! thank you

[@achronicstudent]

I am a rookie MSc student this is my first time learning these.. uh.. whatever these are... and everyone in the comments saying "Woah now i understand great explanation" etc etc. and I am just looking at the screen and feel dumb.

[@alisiena7009]

i have problem with dataSet if very small between [-1;0] and i have the approximation target between [0;1] but always the trainig performance is not go to thebest solution how can i solis problem

[@hson198]

can you explain at 1:03:18 (slide 19) that why 0

[@NicolaPiovesan]

24:00 "so by doing this operation you have done an inner product in a infinite dimensional space. Congratulations!" - LOL :D

[@111rave]

you are a really good lecturer!!! "Okay" :D

[@kennethnavarro3496]

I am not sure but i am pretty sure that the equation for b at minute 36:59 is wrong. When I solved I got almost the same thing except instead of y of m i got 1/(y of m ) n the same spot.

[@ajayram198]

In slide 1 at 5:04 he talks about using SVM with non linear transform. Could someone there explain the difference between h and H? (Complex h but simple H)

[@sddyl]

Fabulous！ Great Intuition!

[@Nestorghh]

Great class! Thanks a lot!

[@nishanthkanala]

Just Brilliant!!!

[@明焕李]

讲的真好！

[@michaelmellinger2324]

@39:50 The whole idea of the kernel is that you don’t visit the Z-Space

[@apeman5291]

56:51 - I don't know, that still looks pretty complicated. 59:01 - Okay, that was pretty neat. 59:29 - Jaw hit the floor.

[@siddharthsvnit]

1:02:30 can't slack still be zero ? as 0*0 = 0 , so condition is still satisfied

[@markh1462]

No, because you're also maximizing beta. So, the only reason we would ever let beta be zero is when zeta is nonzero.

[@mementomori6734]

Mark H I dont understand

[@gcgrabodan]

If you take the derivative of the lagrangian of the soft-margin SVM, with respect to w, why does Xi (the error) drop out? It should depend on w, doesnt it? i.e. different margins will have different errors. So it seems to me like a super complicated problem... Thanks for help ;)

[@Bing.W]

Different margins do have different errors, but different margins do not have different Ws. That's why Xi does not depend on W. In other words, for a same hyperplane (fixed W), you can define different allowed errors (Xi).

[@0xaugustus]

Absolute genius !

[@reinerwilhelms-tricarico344]

I like this a lot for his great clarity. Except this: When you get to "Then call your quadratic programming code to hand over the alpha's", you may end up with a big can of worms, because no body seems to know how to call any of the damn quadratic programming software that is available. There seem to be hundreds of codes around with usually miserable documentation. May be left with role your own. 😁

[@PradiptoDas-SUNYBuffalo]

56:53 - did not see that coming - why was he proud over the equation? 59:46 - memorial service for beta! Classic!

[@jiewang7713]

execellent "OKs"

[@jackeown]

The previous lecture is very helpful for understanding this: kzbin.info/www/bejne/m3nWdqWiha-Ki7c

[@behrozkhan2000]

Ok!

[@roknyakhavein5833]

We R in Z space.

[@diegoiruretagoyenaoliveri6050]

OKAY

[@MohamedAtia]

ok?

[@brainstormingsharing1309]

👍👍👍👍👍

[@nooneknown]

31:20

[@petar29able]

Im to stupid for this why am i here anyway

[@tianchi1989]

This is almost the best explanation about kernel I find. But the tone he uses makes me really sleepy. :(

[@Nestorghh]

haha. great

[@robbertkarry4392]

like russian

[@nha1481]

Who cares?

[@Waynema8]

Great lecture !