

SML: Scalable Machine Learning Class (with video lectures) - adilkhash
http://alex.smola.org/teaching/berkeley2012/

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tikhonj
Heh, because confusing ML the language with "machine learning" wasn't enough,
let's introduce SML so that we can confuse it with Standard ML :P.

The ML/ML conflict actually forces me to backtrack reading some sentences
simply because I always assume the person is talking about the language.

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tjr
For a moment, I was hoping this was going to be a machine learning class
taught using SML... :-/

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amatsukawa
Took this class at Berkeley last semester. Hard but very good, as long as you
have the right mathematical background. Think graduate level math/stats and
not "I took the ML course from Coursera".

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gaius
Is it just me, or does SML already mean Standard ML?

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heretohelp
You'd almost think that people working in CS had narrow specializations...

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dvse
If you don't already understand them, probably a good idea to skip the
detailed derivations and look for the big picture. The course is really quite
hard to follow closely if it's your first exposure to the material.

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bravura
Skimming the slide set #1 (Systems) is highly useful, even for people that
don't do machine learning.

For example, he covers the frequency of hardware failure, and also gives
latencies for different operations (L1 cache read, disk read, etc.)

Slide 25 lists many different types of data on the web, categorized. This
jumped out at me because, reading the list in one big picture got the gears in
my head turning about potential data sources, and what could be done with
them.

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noelwelsh
This is an awesome series of lectures. They were my regular evening listening
for a time. Note some of the earlier lectures don't have sound. That makes
them a bit hard to follow. :-) Also, the later lectures were missing last time
I looked.

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dglassan
Has anyone watched these yet? This is what I've been interested in lately and
would like to know what people think about the videos.

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newtonapple
I watched some of the videos they are quite good. The data streams lectures
are definitely worth a watch:
<http://alex.smola.org/teaching/berkeley2012/streams.html>.

