Stanford CS229 Machine Learning I Supervised learning setup, LMS I 2022 I Lecture 2

[@Dylanlavoue]

Thank you for sharing those lectures! It helps me understand the foundations and everything that follows!

[@stanfordonline]

Awesome feedback, thanks for watching!

[@sjlee5753]

14:30 Boston House Price 17:05 How do we represent 'h'? 22:21 Vector Notation

[@mayankpalan4796]

A great shout out to you guys for releasing this publicly.Just a request can you release all lectures at once.I had started taking the 2019 cs229 a week ago and I have my holidays now so can devote time to it.

[@prabhavkaula9697]

Thanks for sharing the Spring 2022 lectures :)

[@solaris413]

It would be helpful if you provide the problem sets too.

[@kcpillai6367]

8

[@spockle]

2018 problem sets are on github

[@BoredEconomist]

Thank you for posting this!

[@sul9343]

Yes, that should be transpose of the xs vectors.

[@JJ-ds2get-her]

Now, where can I find the homework

[@shayariza3911]

can someone help me, how did he get from the equation in 40:30 to 40:55

[@GameLordTR]

derivative

[@angelmcorrea1704]

I love you share this content. Thank you so much.

[@stanfordonline]

Awesome feedback, thanks for watching!

[@slayr6418]

How did he derive the equation at @34:35?

[@Jonathan-ru9zl]

What introductory course should I watch before attending this one?

[@keshavamurthy2585]

Sir awesome explanation. One query derivative of J(theta) is xj...am i missing something? How cost function gradient be eq to h(x)?

[@LeandroBalbyMarinho]

Excellent lectures, thanks for sharing! Can you share the name of the software you use for whiteboard?

[@Prajwal-zs2gc]

where to get problem sets?

[@ikurious]

Could you please add chapter wise split 😄

[@stanfordonline]

Thanks for watching, good suggestion - we will share your request with our media team :)

[@girikkhullar4072]

Thanks, stanford for sharing the lecture. Could you pls guide me to the psets for this course? I was not able to find them on the course website.

[@stanfordonline]

Thanks for watching and for your question! Currently the problem sets are unavailable publicly, but they will be added to the course website if that changes.

[@solaris413]

@@stanfordonline please add them, its a request without them it will be mostly like a normal YT tutorial not a university course

[@yxj9364]

@@solaris413 Do you know that this course costs Stanford students around 3k-4k USD?

[@solaris413]

@@yxj9364 no

[@oye_pritpal]

where can I find these lecture notes?

[@All_Kraft]

Thank you for uploading this videos! Aren't other 17 lectures unavailable?

[@stanfordonline]

Thanks for watching! We're releasing one lecture a day over the next several weeks, all the lectures for the course will be available to view by the end of the month (August 2023)

[@All_Kraft]

@@stanfordonline cool! This is why we love it!

[@isalutfi]

Thank you for sharing

[@ikurious]

Expected ❤

[@geekyprogrammer4831]

Apologize in advance if I sound rude but I would like to know what sets out this course different from Andrew NG's Machine Learning course on Coursera?

[@vedanthbaliga7686]

I didn't find any difference with this version of cs229 compared to the ML Course on Coursera but the previous version by Anand Avati was certainly more mathematically intensive!

[@geekyprogrammer4831]

@@vedanthbaliga7686 that's a helpful comment! Thank you ☺️

[@__________________________6910]

upload 2023 one

[@amitabhachakraborty497]

lecture is too fast

[@rewu858]

The point of a youtube video is that you set the rythm, I take twice the length of the video to try and assimilate everything said.

[@PerformanceinFootball]

Poorly explained