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Lovely lecture. I was wondering where one could get the set of assignments for this course so as to evaluate one's understanding better. Thanks

Great lecture. But it looks like the audio was collected on the audience's side rather than the professor.

This sucks. You have made a simple topic so much complex

All of this could be explained in a better way and this aint it

I have to say this video course is more clear and understanding than others.

Thank you very much ,your way is fantastic ,keep doing

Isn't x the independent variable and y the dependent variable?

Does anyone know where I can find the assignments?

Read 100 blogs and books and couldn’t 100% comprehend this. But this lecture explain it clearly like day light

Thanks.

Thanks

Thanks. On the slide at time 12:09 is written [perfomrance]. Perhaps the intention was to write [performance].

Thanks. At time 0:28 the slides reads f = g. Was the intention to state f = g₀ , with a subscript?

Thanks. At point 02:02 the slide shows [whlie], while [while] was probably the intention.

amazing visualizations. I learned a lot from the code. Thanks.

could you please post lecture on neural networks professor you are awesome!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!

thanks for the video but there is background noisy, I think you used camera mic.

Question on slide 12: As I understand it, we do know what the denoising process would be for an image. Just substract the noise that was added to generate the noisy version. But we want a NN to learn these parameters so that once it's trained, we can feed it random noise and it will output novel images. Is this statement correct?

thank you so much🤎🤎🤎

Great video!

@34:34 - The last integral on the RHS, why is there an S_theta(x) "square" term ? .. shudn't it just be s_theta(x) ??

It should be -y.x if y.f(x) < 1

On page 16 (around 38minutes): "The probability is non convex". I think the important thing is that the problem is non convex on Mu and sigma. It is true that P(x) is non-convex but this wouldn't be a problem if it was convex on mu/sigma while non convex on X. I got a little confused by the image that shows the mixture is non convex on the x axis and wanted to clarify if anyone also finds useful :) (and hope it makes sense)

the f-divergence part of the lecture is NOT very clear ... :(

great lecture but i wonder the slides look an awful lot like the standford cs236 lecture slides did you develope the course together 🙈

What is meant by parameters? How do you calculate the number of parameters? After 25:56 it is mentioned number of parameters is 2^n-1

Well explained

H(1-\epsilon)^n isn't a probability as is unbounded. To make it a probability must be defined as min(1, H(1-\epsilon)^n).

Great lecture!

I havent seen the best course like that taking from basics, I love this !!

Where to find the assignments? They are absent in the website.

Hi professor, once Neural Autoregressive model(NADE) is trained or for that matter any model like wavenet or pixel CNN, How do i compute the probability of a new image belonging to learnt distribution?

Definitely one of the best courses on the topic !

Can you recommend any specific books that we can follow alongside this course ?

Excellent ! By far the Bestest lecture series on Generative Models simply because it explains the intuition behind all the underlying math .. just fabulous !!

Awesome !

Loving it !! ... Can't thank enough ..

Terrific Lectures ... just bang on the target .. addresses all the issues which are a bit tricky to understand .. Amazin !.. Thanks a ton

Excellent ! .. Thanks ! .. addresses almost all of the pain points pertaining to Probabilitistic Machine Learning

Turing out to be just amazing set of lectures .. exactly touching upon the most painful (to understand) points in "probabilistic machine learning" .. awesome !

some feedback : very shallow , non concrete throw of words without deep explanation. for ex : 1) P(x|y) , no mention of what y is. 2) super resolution and signal processing examples : why is this not representation learning , model still learns a representaiton in these cases 3) imitation learning , again why is this not supervised learning (just because its RL) . Too much usage of jargon and no depth

Amazing lectures, thanks a lot! But I am getting a lot of adds on those lectures. For me its around 30 seconds of adds every 5 minutes. It makes it hard to stay locked in... especially on this lecture, which demands a bit more effort from me

Hey Volodymr -- Any chance that additional lectures will be added? I loved this first one

Hi, where can I get the slides of the course?

its only theory it would have been so much better if done practical side by side too.

One of the most amazing lectures. Ive never seen a lecture on generative models that is so connected like these, from simple autoregressive, to latent models, to gans, to energy-based, langevin dynamics, and finally to diffusion models, all are connected! The connectedness and story telling are so amazing! thank you Prof!

very high quality and amazing lectures, thank you Prof!

Prof could you please upload the slides since your github slides arent updated?

Please use generative AI to super resolution the audio behind this video!

Why is pixel x3 dependent on x1, x2, but not x4..? Okay I get it it is using Chain rule in Probability to decompose the joint probability distribution.. Not saying x3 depends only on x1 and x2, but learni learning that marginal distribution as a way to predict joint distribution.