Lecture by Professor Andrew Ng for Machine Learning (CS 229) in the Stanford Computer Science department. Professor Ng provides an overview of the course in this introductory meeting.
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
CS 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 : 1:8:40
[youtube UzxYlbK2c7E]
25 comments
Comment by wizztjh on April 18, 2010 at 12:16 pm
thanks for posting …
thanks for posting this nice video , it really help me in understanding of machine learning!
Comment by kjsdffkjweh8 on April 18, 2010 at 12:16 pm
Thanks for posting …
Thanks for posting all these awesome lectures guys!
Comment by nielsww on April 18, 2010 at 12:16 pm
Will be watching …
Will be watching them all. Thanks for uploading these great classes!
Comment by matthewrobertson03 on April 18, 2010 at 12:16 pm
I’m wondering why I …
I’m wondering why I spent so much money on a university degree -_-
Comment by TheMr247 on April 18, 2010 at 12:16 pm
Thankyou!! …
Thankyou!! fantastic post!! will be watching them all – and looking out for more
Comment by tleeuwenburg on April 18, 2010 at 12:16 pm
I would just like …
I would just like to echo another “Thank You”.
Comment by BrianVandrian on April 18, 2010 at 12:16 pm
thanks for posting!
thanks for posting!
Comment by notbored12 on April 18, 2010 at 12:16 pm
Machine learning …
Machine learning used to be called “statistical pattern recognition” but a more vague and impressive sounding title probably attracts more funding. AI has a slightly different meaning than machine learning but this comment is too brief to go into detail, google is your friend.
Comment by woodengun on April 18, 2010 at 12:16 pm
fucking chines dog …
chines dog eater! off bro, White supremacy!
Comment by woodengun on April 18, 2010 at 12:16 pm
but still better …
but still better than yours fella!
Comment by rafaelhsouza on April 18, 2010 at 12:16 pm
No.
No.
Comment by someonefromsomewere1 on April 18, 2010 at 12:16 pm
@Compact3
Not …
@Compact3
Not exactly,
AI is just the computer following certain instructions based on predefined circumstances, but machine learning is when the machine starts to learn from its mistakes and don’t make them a second time. (or something like that )
Comment by Compact3 on April 18, 2010 at 12:16 pm
Isn’t AI equal to …
Isn’t AI equal to machine learning?
Comment by Kornmeister on April 18, 2010 at 12:16 pm
“Machine learning” …
“Machine learning” is basically when a machine is able to improve it’s AI on it’s own.
Comment by edyinthesky on April 18, 2010 at 12:16 pm
does he mean AI …
does he mean AI instead of “machine learning”???
Comment by allenst on April 18, 2010 at 12:16 pm
wow the guy has a …
wow the guy has a personality for a wang
Comment by sg04f on April 18, 2010 at 12:16 pm
Dr Ng mentions that …
Dr Ng mentions that we can find the review classes for the prerequisites online. Where are they posted?
Comment by sudo333 on April 18, 2010 at 12:16 pm
Professor Ng is so …
Professor Ng is so great.
thanks.!!
Comment by LogicalErr0r on April 18, 2010 at 12:16 pm
Great job! Thanks …
Great job! Thanks alot to Stanford and thier stuff but I have a small suggest which added subtitles to the video because there are alot of international students who dont understand some words without subtitles. Thanks alot
Comment by therobz98 on April 18, 2010 at 12:16 pm
orm.. orm.. orm…
orm.. orm.. orm…
Comment by MisappliedRhetoric on April 18, 2010 at 12:16 pm
31:30? Good God.
…
31:30? Good God.
Thanks.
Comment by disprefer on April 18, 2010 at 12:16 pm
NOTE: skip to 31: …
NOTE: skip to 31:30 if you’re here to learn about machine learning.
Comment by outspan87 on April 18, 2010 at 12:16 pm
umm… umm…

…
umm… umm…
great lecture
Comment by proximo20 on April 18, 2010 at 12:16 pm
i hope not all …
i hope not all examples are similar to tumor sizes and their malignancy rates.
Comment by moekelok on April 18, 2010 at 12:16 pm
I cannot convey how …
I cannot convey how grateful I am that an academic institution of the stature of Stanford is generous enough to offer fascinating information like this for free. Cheers!