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Title: Lecture 2 | Machine Learning (Stanford)
Added: Jul 22, 2008
Author: StanfordUniversity
Duration: 76:16
Description:
Lecture by Professor Andrew Ng for Machine Learning (CS 229) in the Stanford Computer Science department. Professor Ng lectures on linear regression, gradient descent, and normal equations and discusses how they relate to machine learning. This course provides a broad introduction to machine learning and statistical pattern recognition. Topics include supervised learning, unsupervised learning, learning theory, reinforcement learning and adaptive control. Recent applications of machine learning, such as to robotic control, data mining, autonomous navigation, bioinformatics, speech recognition, and text and web data processing are also discussed.Complete Playlist for the Course:http://www.youtube.com/view_play_list?p=A89DCFA6ADACE599CCS 229 Course Website:http://www.stanford.edu/class/cs229/Stanford University:http://www.stanford.edu/Stanford University Channel on YouTube:http://www.youtube.com/stanford
Related Videos:
Videos related to 'Lecture 2 | Machine Learning (Stanford)'
Channel: Education
Tags: science math engineering computer technology robotics algebra linear regression learning algorithm gradient descent normal equation
science math engineering computer technology robotics algebra linear regression learning algorithm gradient descent normal equation
Youtube Comments: 105
ramoncaldeira22 Says:
Oct 10, 2011 - that is linear algebra, you could check this out on Khan Academy
robertxxx74 Says:
Oct 16, 2011 - That proof at the end (from about 1hr - 1hr 15min) is hard to follow. If you download the lecture notes from the site, it's written down in very few lines. I had to really work to put it all together. It's taken me about an hour to understand it, and even then I'm not fully satisfied that I have proven it to myself. He makes many leaps that are not obvious to me.
robertxxx74 Says:
Oct 16, 2011 - (see prev comment 1st) I often find these leaps in the math used by engineers to "prove" things work. I don't think it's just my lack of background either - he shows the operations he's using, then expects you to take it as read that the math really does work, and expects you to be able to follow right away. You don't usually need to understand the proofs to pass the course though, or to use the results. It's just not as satisfying.
tessb Says:
Oct 19, 2011 - on the course website (google it) it says you need linear algebra and probability theory, but it said you need basic linear algebra and probability and a little programming experience.
shrit110 Says:
Oct 22, 2011 - watch after 9:00
harrycook111 Says:
Oct 28, 2011 - the calculus part is easy enough but he skipped most of the linear algebra details which is a big part of his math. So... you can go to MIT web site for another FREE education from Prof. Strang and the final equation will become very clear.
turkiym2 Says:
Dec 2, 2011 - Mainly calculus and linear algebra, you may pick up the two in 2-3 months if you're intent on learning as they're usually freshman level courses and have no pre-requisites themselves (you may also learn them concurrently as they are independent on the basic level and will intertwine easily as necessary). For Calculus, I recommend the James Stewart textbook, as for linear algebra, I recommend the text by Otto Bretscher. Both are illustrative, thorough and easy to follow.
turkiym2 Says:
Dec 2, 2011 - The asker was clearly sleeping off his calculus classes.
ByThe1Way Says:
Dec 5, 2011 - I know matrices But i was lost after he was talking about proving it.
wpeng001 Says:
Dec 20, 2011 - should add trace to x'x to make it equal to \sum X_{ii}
rahulsanal1 Says:
Dec 24, 2011 - kindly check the link.. i can't view the video.. it says some error has occured for the past two days..
hylandsjgcn Says:
Jan 29, 2012 - 15:10
sdenkasp Says:
Feb 3, 2012 - Thanks to my Linear Algebra course in Peru :), I understood this nice lecture...so I continue with Lesson 3.Thanks Stanford!!!
GlobalDuty Says:
Feb 28, 2012 - if you need more help with the math with Professor Andrew Ng, you can find helpful videos atml-class.org
x89codered89x Says:
Mar 19, 2012 - I watched all these videos before when I was a sophomore in college. Currently a senior, and I am now much more prepared for this!
tonio3lonboard Says:
Apr 19, 2012 - He is wrong at the far end I think when he talks about pseudo inverse. He actually has proven that the pseudo inverse of X (which is the formula which is written on the board) is the solution of the problem. His oral answer to the question about taking the pseudo inverse of XTX is not what the student meant and is not really relevant.
Alexander Hayes Says:
May 18, 2012 - That IS modern math ...
jfhryn Says:
May 19, 2012 - indeed!
jfhryn Says:
May 19, 2012 - Thx so much! I've just signed up for this coursera!I'm excited!
psbbboyz123 Says:
May 21, 2012 - What he says I think is right... He says that if XTX is not invertible which is the case when X is not full rank matrix(he says that X is dependent) then in that case you find the pseudo inverse in that particular case.
mfbx9da4 Says:
May 26, 2012 - wtf! lost after 30:00
astroboomboy Says:
Oct 8, 2011 - This lecture is interesting, but I don't get the math at all. What kind of math is this, and what should I read in order to understand this (my background is linguistics, so I have no math training). Is it enough to learn calculus? And is it possible to really learn math at an older age, or is it like playing violin, you have to start at a very young age in order to become professional?