Linear Regression 1 [Matlab]

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This video describes how the singular value decomposition (SVD) can be used for linear regression in Matlab (part 1).
Book Website: databookuw.com
Book PDF: databookuw.com/...
These lectures follow Chapter 1 from: "Data-Driven Science and Engineering: Machine Learning, Dynamical Systems, and Control" by Brunton and Kutz
Amazon: www.amazon.com...
Brunton Website: eigensteve.com
This video was produced at the University of Washington

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1 окт 2024

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Комментарии : 19

@payman_azari Год назад

I recommend watching the regression sections multiple times; tons of points to learn and to look in the references. Extraordinary tutorials, thanks Steve.

@hassaanhameed 11 месяцев назад

i replay the video after reading your comment I need to watch it again after testing on the real data

@dragoncurveenthusiast 4 года назад

9:18 I think b = a*x would be more accurate than b = anoisy*x. After all, it is the true factor a that has an influence on b, not the noisy measurement we took of a. I think this is the reason why the plot looked so much better than expected, also at 7:58, the noise was added to a before calculating b from a, so the noise didn't influence the relationship between a and b.

@Старкрафт2комедия 2 года назад

wow, good job! I take back what i said about you lacking examples. this is top stuff

@danielhoven570 4 года назад

Just got your book! can't wait to dig in

@Eigensteve 4 года назад

Awesome! Thank you!

@songurtechnology 5 месяцев назад

Thank you ❤

@younique9710 5 месяцев назад

After we added some noise to a, should we run SVD with the noisy a rather than a in the 11th commend of the MatLab?

@fahimmumand Год назад

How can one calculate the uncertainty with svd? Say that we have uncertainty in both a and b, based on the uncertainty the slope and intercept of y should have uncertainty as well, can you please elaborate how to calculate the uncertainty in the slope and intercept. Thanks in advance.

@HuadongWu 4 года назад

the first method demon [time 6:45 - 7:19] is problematic explained as following: [U,S,V]=svd(a,'econ') input a doesn't change slope, so [U,S,V] won't change characteristics, xtilde=V*inv(S)*U'*b won't change its distribution from b's characteristics

@lingnie2809 2 года назад

just out of curious. Is that a 13'' macbook or 16'' macbook?

@jakobjonsson1474 Год назад

When using the SVD command, you input your array a as a 1d array. This doesn't work in python. Does anyone know how to do this in python with a 1d array? Thank you

@jakobjonsson1474 Год назад

I understood what the problem was when watching the corresponding video in Python.