Prof Steve.... Just keep publishing these videos forever :)

If you apply LASSO on lectures of this topic only Steves' videos will survive.

Fantastic lecture, Steve! Probably my favourite one to date...

Thanks for publishing these videos. I'm more of a programmer than a maths person, but it's really nice to have an idea about what algorithms there are out there to interpret datasets.

Since the Covid crisis confined me to home, you have become one of my favorite youtubers. Great succinct explanations with real applicability to problems both abstract and praactical. THANK YOU!!

Nice job! Great visuals. Looking forward to seeing more topics! Thanks for putting your content online.

Wow - The best visualization of the topic i have seen so far, it's just amazing how the world learn today, virtually from anywhere - online.

A wonderful book! I never saw such a combination of book, video, and codes from the author. Everything is clearly explained. I don't know how to express my gratitude in words!

My favourite channel of all time. I hope we're going to get videos on Interpretability for machine learning.

These videos are so much better than any lecture that I had at the university!

Such a great lecture! Deep but enjoyable on a Saturday morning:)Thank you professor.

Thank you for always publishing amazing videos!

You make these topics engaging. Thanks.

I have learned a lot from your videos, Prof. Brunton. Thank you!

Thanks for the clear explanation and ample good examples.

GREAT lecture. Knew most of the content, but had to watch it to the end anyways.

Excellent, as always. Extremely good content.

Thank you for crystal clear lecture. And the topic is fantastic because: a) linear model (simplicity) b) interpretability (for the reasons you have clearly explained yourself). I am looking forward for more content and I am ready to buy yet another your book professor

Excellent presentation Steve.

Hi Professor Steve, thank you so much ❤️.

Loved this. So Sad I discovered this channel so late! Finally, a channel, which doesn't dumb down and help really improve the vigor mathematically as well as conceptually without being daunted by research papers notations and lingo. I request videos on Optimization as a series - how it works in different algorithms across Supervised, Semi, Unsupervised, Reinforcement.

Thanks for this excellent lecture!

Thanks for this great lecture.

This is pure gold...

Thank you for the great contribution.

great lectures!!!!! many thanks!

I used LASSO & Elastic Net for a sports betting prediction model this year in college basketball. The LASSO model did better than EN. Thanks for the explanation! It was very timely for me. :)

just amazing

Awesomeness thank you👍

Is there a talk on SR3? Sounds really cool! Will check out the paper

Thank you for this very helpful video. I was looking for a method for sparse regression and directly used pySindy. However, unfortunately, our data is not suited to be interpreted as a dynamical system. Long story short. From the big possible selection of regression techniques - now I have some kind of overview and now SR3 should be the next step.

Amazing Math visualizations!!! In particular, what software/programming language did you use to create the 3D versions of the Tibshirani plots? (minute 20:00). I think that the intuition behind the Sparsity induced by the L1 norm is much clearer in higher dimensions. It's a shame that we have to stop at 3 dimensions. Still many thanks for the visualization!

Thank you Prof :)

@Eigensteve

8 ай бұрын

Thanks for watching!

Thank you very much Dr. Steve.

Hello Sir It was great video. Thank you for this. May you also make video on SISSO

Thanks

Please make a video explaining ARMAX model estimation method. Thank you.

my fav one, just keep publishing

@Eigensteve

3 жыл бұрын

Thanks!

Love these videos how it is easy to watch and understand, even on morning coffee ☕

Hi Steve could we get a lecture on Sgd stochastic gradient decent and Backpropagation!

Thank you so much, Sir. A very insightful video. Could you please throw some light on how to decide the threshold value of lambda in LASSO Regression? Is it dependent on the number of features? Thanks again, Sir.

Very interesting video, Professor. As you mentioned, the Elastic Net algorithm combines the benefits of the Ridge Regression and the LASSO algorithms. Is there a circumstance in which one would specifically use LASSO, rather than simply always going with Elastic Net? Does Elastic Net require significantly more computation to implement? Are there issues that come with the greater generality of Elastic Net that LASSO doesn't suffer from?

@philspaghet

2 жыл бұрын

I want to know this as well!

Hi Prof Brunton, please correct me if I'm wrong: at 25:43 the least square solution is at lambda = 1 not 0 right? Since 1/0 would throw an error.

@Eigensteve

2 жыл бұрын

Thanks for the comment. Yes, I see the confusion. The x-axis label "1/lambda" is not technically correct. It is just a trend that this increases as lambda decreases, but we shouldn't read this literally as 1/lambda. What I mean is that when lambda->0 in the upper right optimization problem, then there is no sparsity penalization and the optimization will return the least squares solution.

Why is the SINDy spot not located at the minimum of the test curve? You put it instead at the knee of the Pareto curve. In ML, we usually use cross validation to locate the minimum of the loss function for the test dataset.

Dear Steven, it appears that you reinvented (partially) kernel-based system identification, popularized by Dr. Lennart Ljung as ReLS. Essentially, it uses inv(x*x') instead of Tikhonov diagonal loading, which is as optimal a solution as it can get. Imho, it is all about how to formalize your "physical" knowledge of the system. BTW, the ReLS's FLOPS are orders of magnitude lower than for biased estimation, compressed sensing, LASSO, etc.

Hi. Professor, please tell us how we can support this channel, shall we just buy the book/ you would set up a Patreon account?

Sir can u please make a video on restricted isometry property.

15:01 I think here is a lot of detail missing, what are the graphs? Why is one a square and one a circle?

wow!!

can anyone download the book?

what is the reference paper that connects svm and elastic lasso?

@Eigensteve

3 жыл бұрын

Here is the paper: arxiv.org/abs/1409.1976

What kind of app. Do you use in your videos?

@zhanzo

3 жыл бұрын

You can have a similar effect with OBS studio. Add a powerpoint presentation with blue background, and use a blue chroma key to make blue transparent.

@felixwhise4165

3 жыл бұрын

@@zhanzo thank you!

Everything's great here, only thing..the side by side images at 15:00 aren't selling it for me. I get that l1 would be pointy while l2 would be spherical. But you say, and the consensus says, that l2 can intersect at multiple points...yet the image shows a tangent. Are we talking about the not-shown possibility of that blue line cutting through and forming a secant?, but if that's the case then the same could happen for the diamond. This is unclear to me EDIT (20 seconds later lol) : AH! The idea is that the dimensionality of the point of intersection

Economic models are typically dynamic systems of difference equations not differential equations...is SINDY applicable to difference equations?? If we can discover nonlinear systems that generate economic data, that would be awesome...but I guess interpretability would still be limited...:).

I want to do PhD again :)

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