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What does it take to be good at machine learning such as this? In terms of mathematics, and computer science knowledge? I know how to code through self learnin

by Noxchi 13y ago

What does it take to be good at machine learning such as this? In terms of mathematics, and computer science knowledge? I know how to code through self learning, and I've pretty much solely done web development. So I barely know much CS. Also not very good at math. So what are the essential prerequisites you would say are necessary for doing neat, useful stuff with machine learning?

mbeissinger 13y ago
Definitely a solid foundation in linear algebra and statistics (mostly Bayesian) are necessary for understanding how the algorithms work. Check out the wiki portals (http://en.wikipedia.org/wiki/Machine_learning http://en.wikipedia.org/wiki/Machine_learning) and (http://en.wikipedia.org/wiki/Artificial_intelligence http://en.wikipedia.org/wiki/Artificial_intelligence) for overviews of the most common approaches. Also, Andrew Ng's coursera course on machine learning is amazing (https://www.coursera.org/course/ml https://www.coursera.org/course/ml) as well as Norvig and Thrun's Udacity course on AI (https://www.udacity.com/course/cs271 https://www.udacity.com/course/cs271)
elq 13y ago
Math. math. math. Linear algebra. Bayesian statistics. MUST know these inside out, upside down. Vector calculus. Convex optimization. A boatload of machine learning literature. The ideas coalescing into deep learning are based more than a decade of research. If you know nothing about math... I can't imagine getting to the point of understanding deep learning (which is a fairly rapidly evolving area) without at least 2-3 years of very hard work. This class is a reasonable attempt to give a quick intro to one major source for DBNs https://www.coursera.org/course/neuralnets https://www.coursera.org/course/neuralnets understanding this course is a good benchmark.
sown 13y ago
I ran through Udacity's CS373 course first (https://www.udacity.com/course/cs373 https://www.udacity.com/course/cs373). It was neat.

by Noxchi 13y ago

mbeissinger 13y ago

Definitely a solid foundation in linear algebra and statistics (mostly Bayesian) are necessary for understanding how the algorithms work. Check out the wiki portals (http://en.wikipedia.org/wiki/Machine_learning http://en.wikipedia.org/wiki/Machine_learning) and (http://en.wikipedia.org/wiki/Artificial_intelligence http://en.wikipedia.org/wiki/Artificial_intelligence) for overviews of the most common approaches. Also, Andrew Ng's coursera course on machine learning is amazing (https://www.coursera.org/course/ml https://www.coursera.org/course/ml) as well as Norvig and Thrun's Udacity course on AI (https://www.udacity.com/course/cs271 https://www.udacity.com/course/cs271)

elq 13y ago

Math. math. math. Linear algebra. Bayesian statistics. MUST know these inside out, upside down. Vector calculus. Convex optimization. A boatload of machine learning literature. The ideas coalescing into deep learning are based more than a decade of research. If you know nothing about math... I can't imagine getting to the point of understanding deep learning (which is a fairly rapidly evolving area) without at least 2-3 years of very hard work. This class is a reasonable attempt to give a quick intro to one major source for DBNs https://www.coursera.org/course/neuralnets https://www.coursera.org/course/neuralnets understanding this course is a good benchmark.

sown 13y ago

I ran through Udacity's CS373 course first (https://www.udacity.com/course/cs373 https://www.udacity.com/course/cs373). It was neat.