Linear Algebra for Machine Learning

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superharrydude09
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I'm in year 12 and I'm self studying linear algebra to use for machine learning projects. I'm learning this through Gilbert Strang's MIT lectures. I was wondering how much of linear algebra I need for machine learning, as the full course seems quite long.
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mqb2766
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(Original post by superharrydude09)
I'm in year 12 and I'm self studying linear algebra to use for machine learning projects. I'm learning this through Gilbert Strang's MIT lectures. I was wondering how much of linear algebra I need for machine learning, as the full course seems quite long.
Are you implementing your own machine learning algorithms - which ones - or using existing libraries?
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superharrydude09
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I'm trying to implement linear and logistic regression algorithms.
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mqb2766
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(Original post by superharrydude09)
I'm trying to implement linear and logistic regression algorithms.
Both of those are fairly standard algorithms and you could use existing libraries.
Its good to understand how to implement stuff like that yourself though ... but it will take longer.
Linear regression is obviously a core part of linear algebra. If you do it efficiently/robustly, you might be implementing some form of SVD (singular valued decomposition). But few people would write it (well) from scratch. You can obviously do some from of simpler (algorithmically) matrix inversion routines which would require significantly less linear algebra.

So its not a simple question, you can get away with some basic matrix vector knowledge, but have to accept the matrix inversion stuff will be "basic", but that may be good enough.

How much data (variables, exemplars) are you expecting to deal with?
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superharrydude09
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I'm expecting I'll use official government data for my first project, which is to do with unemplyment rates. I'm trying to see if this has any connection with stock prices using linear regression algorithm.
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mqb2766
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(Original post by superharrydude09)
I'm expecting I'll use official government data for my first project, which is to do with unemplyment rates. I'm trying to see if this has any connection with stock prices using linear regression algorithm.
Ok. Id expect the data set isn't large. What programming language? If its python, there is quite a bit of stuff in numpy, which in turn has a fair bit in common with matlab/octave.
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superharrydude09
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I'l be using either python or octave.
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mqb2766
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(Original post by superharrydude09)
I'l be using either python or octave.
In that case you probably don't need a huge amount of basic knowledge about linear algebra. You'll be using the numerical libraries in those languages, you won't be implementing them yourself. Its good stuff to know about, but certainly not essential.
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superharrydude09
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Ok, thanks so much for this info.
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mqb2766
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(Original post by superharrydude09)
Ok, thanks so much for this info.
Tbh, I'd just create a simple dummy data set with a couple of variables and a few exemplars and play around with it in octave and learn how to estimate the parameters, calculate the prediction variance, estimate the parameter error etc .... Do it on a dataset you understand to skill up before hitting a harder, unknown data set.
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