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Introduction to Applied Linear Algebra: Vectors, Matrices, and Least Squares

vmls-book.stanford.edu

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Re: Introduction to Applied Linear Algebra: Vectors, Matrices, and Least Squares

#61

Post by roymurdock »

muhneesh wrote: post #4

I'm e-learning Linear Algebra right now to have a good math foundation for Machine Learning. I was a History and Sociology major in college - so I didn't take any math. If you are like me, and working off an initial base of high school math, I would recommend the following (all free): Linear Algebra Foundations to Frontiers (UT Austin) Course: https://www.edx.org/course/linear-algebra-foundations-to-fro... Comments:…

Would be interested to hear why you’re studying machine learning. Do you see important problems you think it can solve, are you looking to make more $$ as an ML data scientist, or just generally interested in stats/data?

Re: Introduction to Applied Linear Algebra: Vectors, Matrices, and Least Squares

#62

Post by chrispeel »

flor1s wrote: post #5

After reading this book (or during), take a look at the author's (Boyd) video lectures on linear dynamical systems: https://see.stanford.edu/Course/EE263/ There is a lot of overlap between the book and the course.

Also take a look at Boyd and Vandenberghe's book on convex optimization: https://web.stanford.edu/~boyd/cvxbook/

Re: Introduction to Applied Linear Algebra: Vectors, Matrices, and Least Squares

#64

Post by rasmus1610 »

muhneesh wrote: post #4

I'm e-learning Linear Algebra right now to have a good math foundation for Machine Learning. I was a History and Sociology major in college - so I didn't take any math. If you are like me, and working off an initial base of high school math, I would recommend the following (all free): Linear Algebra Foundations to Frontiers (UT Austin) Course: https://www.edx.org/course/linear-algebra-foundations-to-fro... Comments:…

I just love the MIT linear algebra course with Gilbert Strang. Awesome teacher

Re: Introduction to Applied Linear Algebra: Vectors, Matrices, and Least Squares

#65

Post by Cheyana »

anonymous_i wrote: post #16
muhneesh wrote: post #9

Earlier quoted context omitted.

Thanks - I am doing that as well! I've been using MIT OpenCourseware for single variable calculus (and will do the same for multivariable). I fenced the parent post to Linear Algebra to not go too far away from the OP. I will certainly check out the Terrence Parr / Jeremy Howard site, and am super familiar with Khan Academy.

I came across-Calculus Made Easy by Silvanus Thompson,on someones twitter feed. Published in 1910 and far less scary and far more interesting to read than a lot of math text books. https://www.gutenberg.org/files/33283/33283-pdf.pdf

“Considering how many fools can calculate, it is surprising that it should be thought either a difficult or a tedious task for any other fool to learn how to master the same tricks.”

I really wish technical books were still written like this. Though if Thompson posted this on HN as a comment he probably would have been downvoted.

Re: Introduction to Applied Linear Algebra: Vectors, Matrices, and Least Squares

#66

Post by School-Cotton »

CamperBob2 wrote: post #55
School-Cotton wrote: post #53

Earlier quoted context omitted.

But there is the requirement that the other elements be equal to zero. It's right there in the sentence you quoted.

It's right there in the sentence you quoted. True enough, thanks -- fixed. Still a completely bogus definition.

The definition (calling those unit vectors which are members of the standard basis “standard unit vectors”) sounds perfectly reasonable to me.

You haven’t given any good reason why you think it’s “completely bogus” or “plain wrong”.

Re: Introduction to Applied Linear Algebra: Vectors, Matrices, and Least Squares

#67

Post by aaaaaaaaaab »

Prof. Boyd is a great teacher! I highly recommend his course on linear dynamical systems [0] and convex optimization [1] too.

The former is beginner stuff, and while convex optimization is more advanced, both are very engaging and clearly explained, with lots of anecdotes and practical examples!

[0] https://see.stanford.edu/Course/EE263

[1] https://www.youtube.com/playlist?list=PL3940DD956CDF0622

Re: Introduction to Applied Linear Algebra: Vectors, Matrices, and Least Squares

#69

Post by dsiegel2275 »

muhneesh wrote: post #4

I'm e-learning Linear Algebra right now to have a good math foundation for Machine Learning. I was a History and Sociology major in college - so I didn't take any math. If you are like me, and working off an initial base of high school math, I would recommend the following (all free): Linear Algebra Foundations to Frontiers (UT Austin) Course: https://www.edx.org/course/linear-algebra-foundations-to-fro... Comments:…

I agree, Strang's Linear Algebra course is excellent. I worked through the entire course in March/April this year.

I just completed my final exam at CMU in their graduate intro to ML class (10-601). Having gone through the LA course was essential to my success. But equally important (if not more) to ML is a solid foundation in probability.

Re: Introduction to Applied Linear Algebra: Vectors, Matrices, and Least Squares

#70

Post by twtw »

starmole wrote: post #33
VikingCoder wrote: post #2

I'm going to complain about this every chance I get. A 2D vector, we generally store as [x, y, 0]. What's the extra 0? The homogeneous coordinate. A 2D point, we generally store as [x, y, 1]. That extra 1 is the homogeneous coordinate, and since it's there, it means "and apply translations!" If I have a 2D transform, I put the translation component in the last row or column, depending on if you pre-multiply or post-m…

I hope you are just trolling. Homogeneous coordinates are for projection, not for selecting translation.

Homogeneous coordinates allow for affine transformations to be represented and performed with a matrix multiply. This includes perspective projection and translation.

One of the use cases for homogeneous coordinates is certainly to be able to achieve translation.

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