I am under the impression that to learn statistics one must first have a working knowledge of probability theory which rests upon grad level math analysis. Can machine learning be studied without any of that?
Some nice courses there, also check out Dan Cremer's lectures on variational methods for computer vision if you're interested in that sort of thing. There's also a nice series on computer vision for special effects.<p><a href="http://www.computervisiontalks.com/variational-methods-for-computer-vision-lecture-2-prof-daniel-cremers/" rel="nofollow">http://www.computervisiontalks.com/variational-methods-for-c...</a>
That's really nice. Dan Cremer is impressive.<p>Here's a great Laboratory on Amazon ML for Human Activity Recognition (w/ Python). <a href="https://cloudacademy.com/amazon-web-services/labs/aws-machine-learning-human-activity-recognition-21/" rel="nofollow">https://cloudacademy.com/amazon-web-services/labs/aws-machin...</a><p>Totally worth a look.
Some of the videos in the link are cut short, and the full videos are much better. Here's a link to a playlist of the full lectures:
<a href="https://www.youtube.com/playlist?list=PLZSO_6-bSqHQmMKwWVvYwKreGu4b4kMU9" rel="nofollow">https://www.youtube.com/playlist?list=PLZSO_6-bSqHQmMKwWVvYw...</a>
Just want to shout about this very comprehensive course by Caltech professor Yaser S. Abu-Mostafa <a href="http://work.caltech.edu/telecourse.html" rel="nofollow">http://work.caltech.edu/telecourse.html</a>
I like his teaching style, but it seems some of the lecture videos (1.3, for example) are cut off - very frustrating! For anyone watching nonetheless, I recommend going into YouTube and changing the speed to 1.5x.
I like Smola's ML book, and it's great to see a full-depth ML course online, I'll certainly watch some videos. Other than that, the audio quality could be better.