I’ve seen many online explanations of this that try to say the main difference is their purpose. This is quite unsatisfying as an explanation and others that try to explain the difference seem quite verbose and difficult to comprehend for me.
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It might help if you explain a bit why you believe the two are related? Statistics as a branch of study in mathematics and machine learning as applied to computer science don’t appear to have any broad similarities such that describing differences would illustrate too much. Perhaps you have a different idea of what these things are?
It’s like asking what’s the difference between linear algebra and computer graphics. Statistics is a field in math. Machine learning is a class of computer algorithms which apply statistics (among many other things) in a certain way to automatically solve some tasks.
Statistics means trying to logically find a pattern, meaning or evidence based on the results of a test (or more tests) carried a fairly large amount of times.
Machine learning is feeding to a software a test and giving it some “rules”. The software performs the same test a lot of times but “remembering” the outcome of all the previous times the test was performed and using this to change the result of the next tests.
Even more simplified: an example of statistics is asking 10k people what they would do in a certain scenario, and work on their answers. Machine learning is asking a question to a computer 10k times and every single time you do, the computer can take into account the feedback they received from you every single time they answered before.
Statistics is just the use of mathematical probability to make predictions and solve problems. Anyone who knows what formulae to apply can do statistics. Machine learning is the use of iterative steps to get a program that can complete a task, without directly programming what steps should be taken.
Machine learning often *uses* statistics, and statistics exists separate from computers. They are not the same thing used for different purposes.
statistics is the mathematical discipline involved in analyzing large collections of data or trends within data in order to either gain meaningful information about the set or a related set.
Machine learning is a computer science discipline concerned with developing and analyzing methods that allow machines to learn how to solve problems on their own without our direct guidence.
A lot of machine learning methods *use* statistics, but not all of them. Similarly statistics doesn’t involve machine learning in most cases. You calculating the standard deviation of height in your family doesn’t have anything to do with ML.
I’ve seen many online explanations of this that try to say the main difference is their purpose. This is quite unsatisfying as an explanation and others that try to explain the difference seem quite verbose and difficult to comprehend for me.
In: 0
It might help if you explain a bit why you believe the two are related? Statistics as a branch of study in mathematics and machine learning as applied to computer science don’t appear to have any broad similarities such that describing differences would illustrate too much. Perhaps you have a different idea of what these things are?
It’s like asking what’s the difference between linear algebra and computer graphics. Statistics is a field in math. Machine learning is a class of computer algorithms which apply statistics (among many other things) in a certain way to automatically solve some tasks.
Statistics means trying to logically find a pattern, meaning or evidence based on the results of a test (or more tests) carried a fairly large amount of times.
Machine learning is feeding to a software a test and giving it some “rules”. The software performs the same test a lot of times but “remembering” the outcome of all the previous times the test was performed and using this to change the result of the next tests.
Even more simplified: an example of statistics is asking 10k people what they would do in a certain scenario, and work on their answers. Machine learning is asking a question to a computer 10k times and every single time you do, the computer can take into account the feedback they received from you every single time they answered before.
Statistics is just the use of mathematical probability to make predictions and solve problems. Anyone who knows what formulae to apply can do statistics. Machine learning is the use of iterative steps to get a program that can complete a task, without directly programming what steps should be taken.
Machine learning often *uses* statistics, and statistics exists separate from computers. They are not the same thing used for different purposes.
statistics is the mathematical discipline involved in analyzing large collections of data or trends within data in order to either gain meaningful information about the set or a related set.
Machine learning is a computer science discipline concerned with developing and analyzing methods that allow machines to learn how to solve problems on their own without our direct guidence.
A lot of machine learning methods *use* statistics, but not all of them. Similarly statistics doesn’t involve machine learning in most cases. You calculating the standard deviation of height in your family doesn’t have anything to do with ML.
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