Відео

Is it correct that the way we define x on slide 10 the order matters? Otherwise p(x|f) would not be a probability distribution. I think this is quite inconsistent with the way x was used before: Not as some vector in {0,1}^n but as the number of glasses-wearing people -> Would be more consistent to use a binomial coefficient as normalization constant.

Danke fürs hochladen!

Tübingen ML has some of the best educational UA-cam thumbnails. Usually very clean.

Thanks.

good video

Best video so far on ANOVA

1:23:40

next lecture please

The best video on this topic I have found so far by a large margin. Excellent work!

Hi , Please provide code and excercise of this very nice code.

Can we get course website

You are the best teacher in the world thanks

It was mentioned earlier that the product of two GP is another GP only if it is over the same set of variables (x), and that it is some else if it is over two different set of variables (say x and y). Does not this apply to the prediction step at 1:17:11 (from 2nd to 3rd line)?

Great video!

Having watched quite a lot regression videos I can say confidently this is something which sums up and condenses each and every thing for a beginner to grasp linear regression smoothly(see what I did there?). Thank you so much for making this public!

27:20 Really impressive, especially the pronunciation of Kolmogorov's name.

have you tried with silver nano wire networks ?

awesome

sehr gut

Which book do you guys follow?

can we have the homeward or exercises of this course?

Maike hat das toll gemacht!

in 36:40 sum( f_i(\theta) P_i), P has dimension 3N and f(\theta) has 207? how this multiplication is possible?

Awesome class. But the notation in slide 11 is a bit confusing, what is the advantage of having a unity vector and rotation angle on \omega_j? And, is there a typo on slide 11 should \omega² be (\omega_j)²?

where can i get the code ?

this is the first lecture that went right over my head.....something to look forward to

Exceptional Course!!

This is the BEST course in the world on machine learning!! Others start with linear regression being a prob with parameter w. Prof Philipp start with how probability is a measure on sets. there is no other course in the world that explains this much

Exceptional!!!!

At 1:04:13 , ua-cam.com/video/36GT2zI8lVA/v-deo.html

47:00

It is hard to understand CNN.

Gràcies per fer-ho obert!

Thanks a lot for greay content

Thank you Mr. Hennig. You are an outstanding lecturer in a complex field. A wonderful integrative development of ideas. Enables us to grasp the ensemble of concepts. Thank you.

This is masterpiece. The narrative. Coherence, unity. A real beautiful piece of work.

I think this was the most important lecture of the course, pure gold! There is a big Dilema between making better models in the sense of decreasing error in particular datasets and understanding what is being done. I mean, from the research point of view, it does not seem very interesting in just producing better results if it doesnt come with something that is understandable. Actually, as the lecturer pointed out, Deep Learning research is flooded with that type of thing. GPUs are making people lazy, in the sense of just applying heuristics everywhere, wheres the beauty in that methodology?

can we get the slides, if permissible?

Absolutely amazing video course. Especially after looking at other sources I notice how valuable this is. Every video achieves to combine the intuition and math in a concise was. I recommend the videos to anyone who wants to learn about ML.

Point to the surface optimization is really amazing!

M(\vec{\theta}, \vec{\beta}; \mathbf{T}, \mathcal{S}, \mathcal{P}, \mathcal{W}, \mathcal{J})) 😍

At 44:00 why does the professor say this works for any non zero probability set? I feel P(A ∪ Φ) = P(A) + P(Φ) and thus P(A) = P(A) + P(Φ) and so P(Φ) = 0 for any P(A)

1019

Thanks for these amazing lectures..

amazing lecture. Please post more videos.

Thank you sir for the wonderful explanation

I am tired.

Is there a signal processing course published publicly? He mentioned one in the video but I couldn't find it.

This is probably the most complicated course I've watched.

I am afraid.