Further question for myself, can it be self taught without a degree in comp sci/math?
I have a finance degree and knowledge of python/R, but I'm not sure if the concepts are attainable for me. I've had a few stats classes but only one calculus class. ML seems like a very interesting field.
Don't put machine learning on a pedestal. You don't have to have even a full understanding of every little thing as long as you can interpret data.
Most machine learning classifiers are simply understanding how each model maximizes probability to label certain things.
Machine learning in the general sense is very broad. Most models are derived from an understanding of the domain. From there, as long as you can specify the right random variables (mainly through exploration and interpretation just like statistics) just understand how to run the different classifiers. You can start simple and work your way up to even things like deep learning (a buzz word for neural networks)
Not a problem. You are going to want to tackle your fundamentals first.
This includes things like linear algebra and basic matrix operations, statistical inference (correlation, p tests, sampling,probability,..), and then start working your way up the tree for algorithms.
The order in [1] is a good order and will get you familiar with the fundamentals in a fairly easy way. Use [1] for the overviews of different topics as well. Note that [1] isn't a good in depth practice though.
It's a fairly hand holding introduction to machine learning in general. After you get the brief overview down, start looking in to different problems that might interest you.
Depending on how you learn, since you know R, put some things in to practice with R. It has fantastic support for most learning algorithms as direct libraries.
Other than that, depending on what you're interested in (finance, so I'm assuming forecasting?) start looking in to domain specific problems that you're comfortable with.
One thing I've seen people do is use kaggle [2] for the "Knowledge" competitions. Those are essentially tutorials with test datasets.
Since you're in finance, you maybe interested in the signal processing based math that goes in to guessing when something happens within a certain time period.
See [3] for finance specific and [4] for the more general engineering math.
I hope that helps. Independent study is largely based on how you learn and just leveraging resources available coupled with practice in subjects you find interesting to maintain motivation.
Now keep in mind everyone is different. Some like the courses and homework approach, others just like experimenting on something they're interested in to accomplish concrete goals.
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Further question for myself, can it be self taught without a degree in comp sci/math? I have a finance degree and knowledge of python/R, but I'm not sure if the concepts are attainable for me. I've had a few stats classes but only one calculus class. ML seems like a very interesting field.
Don't put machine learning on a pedestal. You don't have to have even a full understanding of every little thing as long as you can interpret data.
Most machine learning classifiers are simply understanding how each model maximizes probability to label certain things.
Machine learning in the general sense is very broad. Most models are derived from an understanding of the domain. From there, as long as you can specify the right random variables (mainly through exploration and interpretation just like statistics) just understand how to run the different classifiers. You can start simple and work your way up to even things like deep learning (a buzz word for neural networks)
Thanks, that's good info and relieves some of my discouragement. How would you suggest I go about learning ML?
Not a problem. You are going to want to tackle your fundamentals first.
This includes things like linear algebra and basic matrix operations, statistical inference (correlation, p tests, sampling,probability,..), and then start working your way up the tree for algorithms.
The order in [1] is a good order and will get you familiar with the fundamentals in a fairly easy way. Use [1] for the overviews of different topics as well. Note that [1] isn't a good in depth practice though.
It's a fairly hand holding introduction to machine learning in general. After you get the brief overview down, start looking in to different problems that might interest you.
Depending on how you learn, since you know R, put some things in to practice with R. It has fantastic support for most learning algorithms as direct libraries.
Other than that, depending on what you're interested in (finance, so I'm assuming forecasting?) start looking in to domain specific problems that you're comfortable with.
One thing I've seen people do is use kaggle [2] for the "Knowledge" competitions. Those are essentially tutorials with test datasets.
Since you're in finance, you maybe interested in the signal processing based math that goes in to guessing when something happens within a certain time period.
See [3] for finance specific and [4] for the more general engineering math.
I hope that helps. Independent study is largely based on how you learn and just leveraging resources available coupled with practice in subjects you find interesting to maintain motivation.
Now keep in mind everyone is different. Some like the courses and homework approach, others just like experimenting on something they're interested in to accomplish concrete goals.
[1] https://class.coursera.org/ml/lecture/preview
[2] http://www.kaggle.com/competitions
[3] https://www.coursera.org/course/compinvesting1
[4] https://www.coursera.org/course/scientificcomp