
Machine Learning Video Library - Learning From Data (Abu-Mostafa) - mikhael
http://work.caltech.edu/library/
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auston
I am doing the <https://www.coursera.org/course/ml> from Stanford by Andrew Ng
& I definitely recommend it.

I'm really excited by all of this free university level material flooding the
web as I never even started college due to financial concerns (aka I didn't
want to get any loans).

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mikhael
Do you know whether Prof. Ng has updated the material since the first run of
the class?

We are still in the honeymoon phase of free, online university courses, so I
think there's been relatively little criticism of what's available now, but
I'll go for it: I was disappointed by the Coursera/Stanford ML class. It was
obviously watered down, the homeworks were very (very) easy, and I retained
little or nothing of significance.

In contrast, the Caltech class was clearly _not_ watered down, and, as the
material was much more focused (with a strong theme of generalization, an idea
almost entirely absent from the Stanford class, as I recall) I feel I learned
far more.

Another big difference: the Caltech class had traditional hour-long lectures,
a simple web form for submitting answers to the multiple-choice* homeworks,
and a plain vBulletin forum. The lectures were live on ustream, but otherwise,
no fancy infrastructure.

So I think that some interesting questions will come up. Do we need complex
(new) platforms to deliver good classes? For me, the answer right now is no --
what clearly matters is the quality and thoughtfulness of the material and how
well it is delivered. Can a topic like machine learning be taught effectively
to someone who doesn't have a lot of time, or who doesn't have the appropriate
background (in CS, math)? Can/should it be faked? I don't think so, but I
think there are certainly nuances here.

* Despite being multiple-choice, the homeworks were not easy -- they typically required a lot of thought, and many required writing a lot of code from scratch.

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bgilroy26
One of the conscious aims of the undergraduate coursera classes has been to
lower the bar (in terms of assumed prerequisites, pace, and scope) in order to
increase participation.

Daphne Koller's Probabilistic Graphical Models was their first graduate class
and it was definitely tougher than other Coursera offerings have been.

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lightcatcher
This. The Coursera PGM class is the only free online class that I've enrolled
in that felt like a similar difficult to a slightly harder than average
undergrad course at Caltech (where I go to school).

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lightcatcher
I took this course last term after reading an introductory book on machine
learning and skimming through Andrew Ng's CS 229 lecture notes. I thought this
class was particularly excellent at emphasizing the theoretical aspects of
machine learning, as well as emphasizing some underlying themes (like avoiding
overfitting with regularization and cross validation). The class didn't cover
as many models and algorithms as many of the other ML classes, but I've found
those relatively easy to learn with the intuition this course gave me.

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mfalcon
I began the mlclass from coursera but I'd like a more advanced approach.

I found some courses(I don't know if there are more):

Andrew Ng Stanford CS229: <http://cs229.stanford.edu/info.html>

Caltech(the one from the OP link): <http://work.caltech.edu/telecourse.html>

Tom Mitchell Carnegie Mellon: <http://www.cs.cmu.edu/~tom/10701_sp11/>

I'm considering following the Tom Mitchell course as it seems to go deeper
into the details, also because it uses a pretty cool bibliography.

What do you think, am I making the right choice?

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tluyben2
I really liked the Google talk <http://www.youtube.com/watch?v=AyzOUbkUf3M>
and there are a bunch of advances in machine learning mixing technologies,
like inductive learning & genetic programming. The Google video also shows
some combinations of techniques to make it learn much faster.

Fortunately I can find videos and whitepapers on all those subjects, but seems
the libraries are all very much in 'the past'. Maybe I don't know about some,
but is there a library/toolbox like Weka which implement all modern & old
algorithms and allow you to play on datasets mixing and matching them? Maybe I
just couldn't find that, but Weka seems to be too primitive for that?

Disclaimer: I majored in AI a long time ago and I understand most of these
concepts, but I have never touched it after I finished, so I'm not up to
date/aware of everything, so sorry if I missed a famous tool or something.

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utunga
If you enjoyed Geoff Hinton's talk you will probably find the theano 'deep
learning' library to be of use. Still undergoing quite a lot of iteration but
powerful and you get to run your stuff on the GPU for added fun.
<http://deeplearning.net/software/theano>. Incidentally Hinton gave another
google tech talk in 2010 <http://www.youtube.com/watch?v=VdIURAu1-aU>.

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tluyben2
Thank you for that deeplearning link; I guess that's my missing link! I did
Google for that many times and it's the first hit, so I have no clue how I
missed that. Anyway, thanks!

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pknerd
Being a newbie in ML, I found intro video quite helpful, having difficulty to
grasp the idea of training, why is it needed etc, I found Mostafa's
explanations quite helpful. I have taken ML's by Ng as well and due to heavy
use of stats I could not grasp it.

Now I am learning Stats by Prof.Thrun at Udacity I assume I will be able to
grasp it in much better way.

p.s: anyone is trying to learn ML basics and wish to learn? Why not learn
together and solve the interesting problems together? contact me via email
given in profile

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raju
I am not sure if this helps but posted yesterday on HN -
<http://news.ycombinator.com/item?id=4200931> (Good for newbies like myself)

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craig552uk
No genetic algorithms :( I love genetic algorithms.

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greenonion
Although certainly a related field (to the point that the UCL ML MSc offers an
Evolutionary Computation course), genetic algorithms are not ML per se.

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tansey
I strongly disagree, though it depends on your definition of what machine
learning really is.

If you define machine learning algorithms as those that learn from data, then
okay. In that case, EAs are reinforcement learning algorithms and other
methods like Q-learning are also not ML algorithms.

However, I use the canonical definition by Mitchell: A computer program is
said to learn from experience E with respect to some class of tasks T and
performance measure P, if its performance at tasks in T, as measured by P,
improves with experience E.[1] In this case, it's clear that in fact EAs are
very much so an ML method. One could even go so far as to say they are more
appropriately ML than things like SVMs as they are truly learning from
experience rather than just data being handed to them.

[1] <http://en.wikipedia.org/wiki/Machine_learning#Definition>

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greenonion
I don't necessarily disagree with you. However, I find that most contemporary
real-world ML applications (from face recognition to collaborative filtering
to computer vision) do use algorithms that "learn from data", using some kind
of learning technique, i.e. supervised, unsupervised or a combination of
those.

The process of creating that kind of algorithm is also very different: it is
based heavily on mathematical/statistical/probabilistic methods and hence the
resulting algorithm can be proven that it works with some kind of certainty.
On contrary, creating an EA is mostly some kind of "art" (as one UCL professor
put it).

All in all, I can't help but feel that even though they are both approaches to
the same problem ("how can a computer program learn?"), data-driven methods
and EA algorithms don't share much more. And since the results produced by the
former are what most people expect of an ML algorithm to accomplish, I tend to
think of those when using the phrase "machine learning".

But it's all about semantics in the end.

// I noticed that you are an ML PhD student. So you know exactly what I'm
talking about.

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ahlemk
this links summarizes it all! other links are needed in order to strengthen
your knowledge!

