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 claims 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 reflections. 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 but good work is hard.
Edited: I was wondering if a LLM reading Lecture 7 would detect what was missing in the proof. I tried with deepseek but its first feedback on the Lecture 7 was positive, then when prompted about the incomplete proof it recognize it as a common error and explained how to complete the proof. Also I have to prompt about the bad factor 2 for it to detect it. So it seems that deepseek is not a useful tool to judge quality of math content. Perhaps one could prompt it in special way. Anyway here is my small chat: https://chat.deepseek.com/a/chat/s/34f2e274-528f-4e23-9f3e-c...
staplung · 11h 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 but good work is hard.
Edited: I was wondering if a LLM reading Lecture 7 would detect what was missing in the proof. I tried with deepseek but its first feedback on the Lecture 7 was positive, then when prompted about the incomplete proof it recognize it as a common error and explained how to complete the proof. Also I have to prompt about the bad factor 2 for it to detect it. So it seems that deepseek is not a useful tool to judge quality of math content. Perhaps one could prompt it in special way. Anyway here is my small chat: https://chat.deepseek.com/a/chat/s/34f2e274-528f-4e23-9f3e-c...
https://github.com/michiganrobotics/rob101/tree/main
As a reference, it looks very useful.