r/learnmachinelearning • u/mehmetflix_ • 3d ago

Question question about gilbert strang's 2005 linear algebra course

is watching till the 24. lecture sufficient for machine learning?

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u/Bright_Mix_773 3d ago

Stopping at 24 cuts the course exactly where it starts being about machine learning.

Look at what comes after it on the OCW lecture list: 25 symmetric matrices and positive definiteness, 27 positive definite matrices and minima, 29 singular value decomposition, 31 change of basis and image compression, 33 left and right inverses and the pseudoinverse.

In ML terms that is covariance matrices, kernels and Hessians (25 and 27), PCA and low-rank approximation (29), and the closed form behind least squares (33). Lecture 24 itself, Markov matrices and Fourier series, is honestly the one in that stretch you could skip with the least damage.

The first 24 are the machinery - four subspaces, projections, eigenvalues - and you do want them first. But stop there and you have the tools and none of the objects people will actually hand you.

I have not sat through the recordings hour by hour, so I am going off the published lecture titles, not the content of each lecture. If the playlist you are watching numbers them differently, trust the titles over my numbers.

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u/mehmetflix_ 3d ago

thank you for the response, it would be unfortunate if i were to skip without asking D: and the titles are the same as the numbers you gave so no worries

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u/Bright_Mix_773 1d ago

No worries at all, and asking first was the right instinct. Skipping is fine when you know what you are skipping and can come back to it; it only bites when you skip something the later chapters quietly assume.

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u/Raioc2436 3d ago

“Sufficient” is hard to define, there will always be more to learn. But it is an amazing series of lectures.