I just selected lecture 07 to take a look: Lecture 07 is about QR factorizacion and Householder reflections. The author proves how to construct a reflection to make zeros in the first column and then he just say that following this procedure for the other columns finish the proof. But he should prove or justify why the other reflections do not destroy the zeros of previous reflection. Also he proves that a vector v is the vector to construct the reflection (but there is a factor of 2 that was not correctly simplified, maybe a latex error), but I think that it should be more general and easier to prove that for any w the vector from w to its image f(w) is the orthogonal vector to the plane of the reflection.
I thank the author for the slides, but this little proof need some more care, I don't know about the quality of other sections or the overall quality of the slides. Anyway I like how he tries to make things easy.
staplung · 10h ago
Not exactly the same material but U. Michigan has their Robotics 101 course up as well: Computational Linear Algebra, also in Julia.
This is a nicely comprehensive course, but it looks like it is pretty fast paced, especially in the last few lectures (some of those later slides definitely aren't finished).
I thank the author for the slides, but this little proof need some more care, I don't know about the quality of other sections or the overall quality of the slides. Anyway I like how he tries to make things easy.
https://github.com/michiganrobotics/rob101/tree/main
As a reference, it looks very useful.