Lecture by Professor Andrew Ng for Machine Learning (CS 229) in the Stanford Computer Science department. Professor Ng lectures on Newton’s method, exponential families, and generalized linear models and 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
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 : 1:13:7
[youtube nLKOQfKLUks]
12 comments
Comment by rewtnode on April 29, 2010 at 11:53 pm
It may look like …
It may look like he’s lost in the woods, and the handwriting gets really bad, but then read the lecture notes (on his webpage): That’s very clear and well presented material as far as I’ve seen it. I think his preparation is excellent when writing those notes, i just can’t read his scribbles – so going over the lecture notes becomes crucial.
Comment by NorwalkPost on April 29, 2010 at 11:53 pm
He kind of lost for …
He kind of lost for forest for the trees in this lecture. He went into elaborate detail about the exponential family of funcitons without really making clear WHY we were doing this.
No disrespect to Prof Ng intended, though. As an educator myself, I appreciate how tricky it is to bring this much material together and see it through the eyes of the intended audience.
Comment by pinochet222 on April 29, 2010 at 11:53 pm
lol they ask pretty …
lol they ask pretty good questions, but he can’t answer.
this is a very superficial lecture …..
Comment by gekorio on April 29, 2010 at 11:53 pm
are the discussion …
are the discussion sessions on line?
Comment by 1888junkteam on April 29, 2010 at 11:53 pm
excellent work!
excellent work!
Comment by naughtyamit007 on April 29, 2010 at 11:53 pm
its awsum……one …
its awsum……one of the best lecture
Comment by netheron on April 29, 2010 at 11:53 pm
Typically in …
Typically in machine learning, the likelihood function is expressed in terms of its logarithm. Since the logarithm is monotonic the maxima and minima are in the same location, but the signs are switched, so typically whether it is maxima or minima is understood from context.
As far as the problem of local minima, simple logistic sigmoid problems don’t have them too often. For more sophisticated problems though, it happens a lot. Approximate stochastic methods are used then.
Comment by crazybeautifulworld on April 29, 2010 at 11:53 pm
wtf i s this guy on …
wtf i s this guy on abart what a load aof shite
Comment by saeedanwar77 on April 29, 2010 at 11:53 pm
The derivation of …
The derivation of newton method shown there is for minimising f(theta).
Then suddenly, f is replaced by L’(theta) in the hope that when L’(theta)=0, L(theta) attains its maximum.
First off, the way it was shown, L(theta) should be minimised. But even if it can be maximised, how can first derivative=0 be enough to tell if we have reached the local maximum? It can be any of the minimum, saddle point or the maximum?
Comment by Scutchris on April 29, 2010 at 11:53 pm
I always wonder why …
I always wonder why newton’s method can converge once it is close enough… If there is a point where f ‘ (theta) is zero or near to zero, then the next point can fall far away.
Comment by lqk1985 on April 29, 2010 at 11:53 pm
0：46：50
it is …
0：46：50
it is not proof the pram = thita T * X,
but the pram is defined as thita T * X previously.
Comment by 11111lololol11111 on April 29, 2010 at 11:53 pm
first post lol
first post lol