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=A89DCFA6ADACE599
CCS 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
Duration : 1:16:16
[youtube 5u4G23_OohI]
25 comments
Comment by Gaiacarra on April 12, 2010 at 10:38 am
@Fusionicon Basic …
@Fusionicon Basic Calculus. Other than the weird stats stuff he brings into play when formulating the error function (”J”), you don’t need anything else, so long as you really pay close attention.
Comment by Juefawn on April 12, 2010 at 10:38 am
The best thing …
The best thing about video learning is the ability to rewind the course.
Comment by wizztjh on April 12, 2010 at 10:38 am
it’s just like …
it’s just like study in Stanford! Although it is not physically , but i really let me gain more knowledge of machine learning that only from my university. And he is really a good lecturer!
thank you for you guys that propose it to the Standford University and upload it!
Comment by juludd on April 12, 2010 at 10:38 am
Could someone …
Could someone explain how to get Gradient tr ABAC^T=CAB+C^TAB^T? I can’t see how you can get an addition on the right hand side. At least not from within the rules he described in the lecture. Could one use the chain rule for derivation?
Comment by Mootsterdotcom on April 12, 2010 at 10:38 am
@harkes12
Actually …
@harkes12
Actually, I found the intelligent vehicle quite interesting…
Comment by Fusionicon on April 12, 2010 at 10:38 am
I am so interested …
I am so interested in machine learning,I can program in java and I have MatLab student version. What previous Math would I need to better understand the tutorial?
Cheer.
Comment by 1888junkteam on April 12, 2010 at 10:38 am
excellent work!
excellent work!
Comment by caesiume on April 12, 2010 at 10:38 am
I’m getting a …
I’m getting a Stanford education for free B) awesome
Comment by praneeta133 on April 12, 2010 at 10:38 am
These videos are …
These videos are brilliant!!Andrew is super cool at teaching, thanks Stanford!!
Comment by kanobi14 on April 12, 2010 at 10:38 am
Awesome lectures.. …
Awesome lectures.. I realise how good the Stanford and its professors are. Thanks a lot for believing in open education. May the force be with you.
Comment by outfile on April 12, 2010 at 10:38 am
he not just good… …
he not just good…he is excellent.
Comment by munmuwang on April 12, 2010 at 10:38 am
이거 도대체…머~꼬!
이거 도대체…머~꼬!
Comment by harkes12 on April 12, 2010 at 10:38 am
Its safe to skip …
Its safe to skip first 9.30 min …
Comment by vinhbt123456 on April 12, 2010 at 10:38 am
thank you very much
thank you very much
Comment by hnomier on April 12, 2010 at 10:38 am
Thank you stanford …
Thank you stanford …really great work …The lectures are great
Comment by Foolean on April 12, 2010 at 10:38 am
He’s really good at …
He’s really good at teaching.
Comment by realmadridvideos on April 12, 2010 at 10:38 am
Thank you Stanford
Thank you Stanford
Comment by ninjakannon on April 12, 2010 at 10:38 am
stanza2200, look in …
stanza2200, look in the video description.
Comment by arran5498 on April 12, 2010 at 10:38 am
Stanford. Thanks …
Stanford. Thanks for posting these lectures! Big thank you!
Comment by mayaahmed on April 12, 2010 at 10:38 am
Really nice. Well …
Really nice. Well taught. I am really enjoying listening to these lectures. A true service to public.
Comment by stanza2200 on April 12, 2010 at 10:38 am
Where are the …
Where are the lecture notes posted?
Comment by bidexue on April 12, 2010 at 10:38 am
good
good
Comment by Scutchris on April 12, 2010 at 10:38 am
Yeah, that’s why …
Yeah, that’s why there’s a negative sign.
Comment by tcyue on April 12, 2010 at 10:38 am
gradient of f( …
gradient of f(x.vector)= max increase rate. However the negative of it is called gradient decent
Comment by siddharthbatra on April 12, 2010 at 10:38 am
what you are saying …
what you are saying is right but it depends on your update rule as well. The update rule for descent has a negative sign in itself,
theta_i+1 = theta_i – alpha * gradient
so your above example would be
x_i+1 = x_i – alpha * 2
y_i+1 = y_i – alpha * 2
where x_i = y_i = 1
with an alpha of 0.5 your parameters become
x_i+1 = 0 , y_i+1 = 0
but yeah you are correct. He maybe had the update rule in his mind when he said that.