A conversation with Gilbert Strang [video] | Hacker News Reader

A conversation with Gilbert Strang [video]

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In 2009 I was applying to grad schools and was a complete nobody shmuck. I was walking through MIT on a grad student visit and we went past the offices of both Gil Strang and Sigurdur Helgason and I told the grad student guide that I just had to give it a try to say hello.

Not only did Gil Strang say hi, to some totally random nobody, but he invited me into his office, asked me about my undergrad studies (he even knew one of my undergrad professors well), and said a lot of nice and encouraging things about sticking with mathematics and finding a career. Siggy Helgason wasn’t in, unfortunately, but I remember Gil Strang referred to him as “the national hero of Iceland” and said I should say hi to him too since I was interested in differential geometry.

What a guy! So gracious with his time in a way that too few academics are (which ended up being part of why I quit a PhD program about 4 years after that talk with Gil Strang and started working instead).

Here's the link for his upcoming book and new MIT course videos that be will posted online in a few weeks:

Linear Algebra and Learning from Data

http://math.mit.edu/~gs/learningfromdata/

Any news on the release of the book? Where to buy and when? much appreciated.

I think he’s still busy writing it.

The book is "expected to appear in December 2018" [1] (send an email to the address located on the cover page below to get updates / early notification)...

[1] http://math.mit.edu/~gs/learningfromdata/dsla_dlnn.pdf

Thank you!

His linear algebra video lectures from 1999 are fantastic: https://ocw.mit.edu/courses/mathematics/18-06-linear-algebra...

I used them ~2006 when I was college. Someone I know who just graduated this year in mechanical engineering also found and used them independently from me.

In my social circles, we've always talked about Gilbert Strang as the best math professor who never actually worked at the university we went to.

> we've always talked about Gilbert Strang as the best math professor who never actually worked at the university we went to.

I very much relate to this. I've never attended the same school of any of my favorite professors.

Forever grateful to this man. 18.06 is my all time favorite online course, it's hard to beleive it was recorded so many years ago.

Was so grateful that my linear algebra professor at Drexel back in 2004 managed to get Strang, the author of our textbook, to take Amtrak down to Philadelphia for a guest lecture. Learned more that day than any other, and gladly waited after class to have him autograph my textbook!

I remember watching the MIT OCW videos for linear algebra and thinking ... this guy came to the Earth to teach this subject. Amazing intuition.

EDIT: Makes me wonder .. what are other examples of teachers who teach their subject soooo well.

> this guy came to the Earth to teach this subject

Exactly that!

I wish he would do an online course on advanced stuff like presented in Golub and van Loan.

He's a great teacher but I prefer to see linear algebra from the point of view of linear maps/operators/transformations and matrices as their representations. His approach goes rather from matrix arithmetic as far as I know.

Is there an online course that covers linear algebra in that way?

Not an online course, but Linear Algebra by Serge Lang is nice little book that is in this spirit (good to look at if you’re familiar with LA basics).

3blue1brown has great linear algebra videos that take it from the transformations point of view.

Gil Strang taught 18.06, the more calculation-focused and elementary version of linear algebra, when I was at MIT. The approach mentioned by the OP was what was taken in the more theoretical version, 18.701. I think that class has notes and lectures on OCW.

To each their own, but I think there is value in seeing them multiple ways. The rows can be looked at as a system of equations. The columns can be vectors to be combined. The matrix as a whole can represent the covariance of a Kalman filter or describing a confidence ellipsoid. They can be an adjacency matrix describing the edges of a graph. They can be your entire data set, and so on. Some of these uses feel more like operands than operators.

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