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SML: Scalable Machine Learning Class (with video lectures)
- dglassan 14y agoHas anyone watched these yet? This is what I've been interested in lately and would like to know what people think about the videos.
- newtonapple 14y agoI 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 http://alex.smola.org/teaching/berkeley2012/streams.html.
- bravura 14y agoSkimming 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.
- tikhonj 14y agoHeh, 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.
- tjr 14y agoFor a moment, I was hoping this was going to be a machine learning class taught using SML... :-/
- gaius 14y agoIs it just me, or does SML already mean Standard ML?
- heretohelp 14y agoYou'd almost think that people working in CS had narrow specializations...
- amatsukawa 14y agoTook 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".
- noelwelsh 14y agoThis 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.
- dvse 14y agoIf 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.