this series is a work of art. needs way more views.
@rajupowers4 жыл бұрын
Most intuitive explanation of the topics in classroom
@clementpeng4 жыл бұрын
Love this. Probably the clearest explanation i have seen on GP online.
@deltasun4 жыл бұрын
thank you very much! I've tried a couple of times to understand GPs, but always gave up. Now i think they're much clearer to me. very very greatful
@raedbouslama22633 жыл бұрын
The previous video and the current one are the best material I watched on Gaussian Processes! Wonderful :)
@peterhojnos67053 жыл бұрын
definitely! I saw many, but this one is one of the best
@abhinav95613 жыл бұрын
Prof Killian killin it! Thanks prof for all the lectures. This course should be the first introduction to the Machine Learning world for everyone
@chamaleewickrama32763 жыл бұрын
Omg. I love this lecture material. To the point, clear and the best!
@saikumartadi84944 жыл бұрын
awesome simulation of a beautiful application !
@Illinoise8884 жыл бұрын
This helps me with my exam preparation, thank you.
@isaacbuitrago23704 жыл бұрын
You make it look easy ! Thanks for the clear explanation of GP.
@atagomes_lncc_br3 жыл бұрын
Best and simplest explanation of GPR.
@AlexPadula5 жыл бұрын
Thank you very much, these lectures are really useful.
@vaaal884 жыл бұрын
this is such a great lesson. Thanks!
@udiibgui21363 жыл бұрын
Thank you for the lecture, very clear! Just one question, how does the Bayesian Optimisation already have a mapped surface?
@kilianweinberger6983 жыл бұрын
initially that is just a flat surface, which is an uninformed prior.
@chaowang30933 жыл бұрын
This guy is brilliantly funny.
@sarvasvarora3 жыл бұрын
Living for that "YAY" 😂😂
@Biesterable5 жыл бұрын
Hm isn't there maybe a way to do low-dimensional egg-search (if it's a manifold there should allways be some main directions) so for the start just make it elipsoid in just one dimension and for comparing distort the room so the elipsoid you're comparing with becomes a globe hm...
@andreariboni4242Ай бұрын
dead mouse got me
@TeoChristopher4 жыл бұрын
To Clarify, for 26:19 , for a Gaussian Process, each data point on the X-axis would we a queried test point , the grey region would be the standard deviation and the points that we have not "queried" would be fitted according to its respective determined distribution which it itself would be a Gaussian distribution with its own mean and s.d?
@kilianweinberger6984 жыл бұрын
Exactly :-)
@mertkurttutan28772 жыл бұрын
Question: Regarding hyperparameter search via GP, I recall that the earlier steps in hyperparameter search involves determining the scale of hyperparameter. How should we determine the scale? Should we use GP for both scale and minimal value at the same scale. Or, Use grid search to determine scale and then, use GP to find the value of hyperparameter. Thanks for both rigorous and enjoyable lectures :)
@akshaygrao77 Жыл бұрын
U keep running bayes optimization which uses gaussian processes, with more iterations it converges to smaller scales itself
@ayushmalik70932 жыл бұрын
hi Prof In Bayesian Optimiser I assume that algorithm for which we are trying to find out best hyper-parameters should be costly enough otherwise it will not make any sense to use GP on top of another algo.
@kevinshao91487 ай бұрын
9:30, so for my test data, y_test, it has 1) its own variance, 2) n correlations with respect to all observed data y1...yn, then how to determine y_test distribution? how did you get the conclusion at 11:06? Thanks!
@chenwang66844 жыл бұрын
Awesome lecture! One question is are the projects available for public? I have found homeworks but no coding projects.
@kilianweinberger6984 жыл бұрын
Sorry, I cannot post them. The projects are still used at Cornell University, and if they were public someone would certainly post solutions somewhere and spoil all the fun. :-(
For the hyper parameter search, wouldn't the bayesian optimization approach be more likely to get stuck at a local minimum?
@kilianweinberger6984 жыл бұрын
No, Bayesian optimization is global. The exploration component makes sure that you don’t get stuck.
@salahghazisalaheldinataban56322 жыл бұрын
Seems from your explanation that the covariance matrix is a simple kernel/distance matrix that does not take into account variable importance. (1) Does that cause any issues if there are variables that have no significant prediction value?, (2) Does it mean we have to be careful about variable selection? And (3) is there a way to incorporate feature importance in the kernel?
@kilianweinberger6982 жыл бұрын
For the linear kernel that's not an issue (as your algorithm becomes identical to linear regression where you learn a weight for each dimension), however for non-linear kernels that can indeed be a problem. One common trick is to multiply each feature dimension by a non-negative weight, and also learn these weights as part of the kernel parameters.
@sandeshhegde91435 жыл бұрын
KD Tree starts from kzbin.info/www/bejne/eKure2hthqiXjNE
@vatsan164 жыл бұрын
One thing I would like to ask is, "what's the catch?" The algorithms seems great but where would we not want to use GPR? Is it in situations where we would like to actually know what the function is? Or are there some situations where GPR wont work well?
@kilianweinberger6984 жыл бұрын
Well, I wouldn’t recommend them for data that is very high dimensional (e.g. bag of word vectors, or images in pixel space). Also, when features are sparse splitting along features becomes tedious and too restrictive, as almost all samples always have zeros in all dimensions.
@giraffaelll3 жыл бұрын
He clears his throat a lot
@thecelavi5 жыл бұрын
Is it possible to use B/B+ tree instead of simple binary tree?
@hassanshakeel8545 жыл бұрын
Are all these lectures dependent on previous ones?