@PhilMcCrack1
Stars at 1:22
@vasileiosmpletsos178
Nice Lecture! Helped me a lot!
@saikumartadi8494
thanks a lot for the video .please upload other courses offered by you if they are recorded. would love to learn from teachers like you
@rameezusmani1294
sir..i cant explain in words how helpful your videos are and how easy they are to understand....people like you are true heroes of humanity and science...thank you so much....i have 1 question...these days machine learning seems to be limited to deep learning using neural networks...i want to know that do people still use ML or MAP or Naive Bayes ?
@giulianobianco6752
Great lecture, thanks Professor! Integrating the online Certificate with deeper math and concepts
@yukiy.4201
magnifico!
@doyourealise
sir did u find out where the handouts went? 10:30??
@sooriya233
Brilliant
@benw4361
Great lecture!
@fuweili5320
learn lots from it!!!
@mehmetaliozer2403
Thanks for this amazing lecture 👍👍
@coolarun3150
awesome!!
@andresfeliperamirez7869
Amazing!
@maharshiroy8471
I believe in practical implementations, a poorly conditioned Hessian can cause huge numerical errors while converging, ultimately lending second-order methods like Newton very unreliable.
@mikejason3822
Why was an approximation of l(w) (using tailor series) was used at 13:20?
@adosar7261
At 39:53 the example seems a little bit off to me. I would expect Newton's method to converge in a single step (the function looks like a quadratic).
@JoaoVitorBRgomes
At 23:00 (circa) how do we check if the function is convex?
@meghnashankr9340
just saved me hours of digging into books for understanding these concepts...
@analyticstoolsbyhooman6963
Heard a lot about Cornell but got the reason for that.
@KulvinderSingh-pm7cr