Amazing!

El peor camarógrafo que he visto en mi vida

u are really great sir ! these are really awesome lectures !

thanks for the great class

32:28 "It's getting crazy right, everything is a vector space..." 😅 It's vector spaces all the way down!

1) data sparseness & overdispersion, do they connect? 2) if we collapsed/combined some categoria (rows, collumns, cells) for the sake of the parsimony & inerpretability & agriment with X2 assuptions (mne>=5 (3?)), minimization of overdispersion, etc., ... does a some "stop" rules exists or may be considered?

may be: Rdf=N-K?😶

First of all, thank you so(×2) much for uploading this lectures course on linear algebra. Time stamp: 37:20. I would prove uniqueness of additive identity in the following way: let 0 and 0' be additive identity of V. By definition of additive identity, 0=0+0'=0'. Thus 0=0'.

omg..all in one lecture that liberates my understanding

It’s 2022. I’m struggling in upper-division Proof-Based Linear Algebra. I earned an A in lower-division Linear Algebra since it was computational-based for the most part, but writing proofs is a whole another beast. This comment may be years late, but this video has been very helpful. Thank you for sharing.

I am endlessly grateful for you sharing these lectures. Thank you so much, this playlist has exposed a lot of my unknown unknowns that have been haunting me for years.

Will W+Z not be W? 20:23

Foundations clearly communicated for a whole branch of statistics.

Professor reminds me of Elon Musk

Thank You Prof. It helped me tremendously.

Thank you!

way too abstract n formulized, hard to understand

He needs to work on his lecture skills. Of course hes apdo so give him benefi of doubt.... but still.. if u cant do the job

Springer has made the book available online: link.springer.com/book/10.1007/978-3-319-11080-6

Video zoom too much, so I couldn't see the whole blackboard.

This is great

I had problems understanding what adjoints are, but you explained it just perfectly. Thank you so much for this classes.

Good lecture, but the class doesn't seem to care lmao

In proof I think We have ORTH[Null(f)]=closure[range(f^{*})] Is it correct?

Lecture Notebooks - github.com/jasonmorton/504notebooks

I think there is another proof of Riesz theorem (showing that a given function is an isomorphism of same-dimension spaces), which is a little bit more direct. Thanks a lot for your video, the definition of adjoint is very clear !

Words for the ages 30:10 " There is no such thing as missing data. Just a misspecified model." I haven't seen this topic presented this way, I really liked it. Got pointers for reading up more on this presented this way?

I have a question. Is there a precedence of taking a random sample of one category of the dependent variable so as to have similar proportions to the second for a three category dependent variable. My category proportions are 0.77, 0.20, and 0.027. Is there any other way to model the three category dependent variable for these proportions.

dear professor can you till us the text u use in this course

Nice

I tried other videos on youtube and the audio works fine, but this set of videos have sound only on the right side of the headphone. Is there a problem with the audio?

Hi, are there any good problem sets/exams that go with these lectures?

In the first 10 minutes this video answered all the questions I was left with after reading the first two sections of chapter 5 in Axler. Thank you.

Awesome lecture. Thanks.

Thank you for this lecture

This lecture is gold

Great lecture. What book are you using for these lectures ?

This is very simple, yet amazingly put together. Thank you!

Hi thank you for the great lectures, in 23:00 we do not need to assume u_1 is different from zero , since we already made the assuption that the u's are linearly indep . amazing teacher !,I hope you can do lectures on more algebra topics ! or any other advanced math topics. subsribed to this channel :)

so its called the rank nullity theorem but you still use range/image instead of rank to define "dim(range(V))"

On point

This was very clear. Thank you!

where is he? if it is possible to tell me this in order to look for more related information. I consider their classes really impressive, but to start I need some source with easier information to process. thank you

What's the name of the professor? Does he have other courses online?

Sensing some uncanny resemblance to Mr. Musk. :D

Could any one provide the notes of the lectures ?

Please provide the notes of all the lectures.

His ponounciation of words was a bit sharp (or maybe low quality of my headphone), but it's so much better with 1.5 speed :D Try 1.5 speed!

Thanks the lecture was awesome, really enjoyed it!!

Are you using linear algebra done right by Sheldon Axler in these lectures?