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What kind of math skills? What would a programmer need to learn in order to work on ML stuff?

by suneilp 15y ago

salimmadjd 15y ago
Aside from algebra to do log or exp division addition and multiplications, you have to be versed in statistics. Most ml problems are solved on statistical bases. Although many of the algorithms have been solved, you still need to grasp the statistics behind it which is a bit more involved than calculating the odds of a die
- chaostheory 15y ago
  Yes, many people that I know working on ML do not remember statistics well enough. Some keep their ignorance while relying on mathematicians on-staff (who can't really code), while others either start buying college textbooks or go to night classes. There are too many people who don't understand the algorithms being used.
Dn_Ab 15y ago
To work with ML stuff you don't need much. You can just download packages and get experience with how to best pick models, choose features and tweak (hyper)parameters. If you want to work on or understand it then you will need math. You need a decent understanding of calculus (mid 1800s level, mulitvariate calculus), a more decent understanding of Linear Algebra (1950s ), information theory (1960s), and probability and statistics. With the last having shifted the most from the past due to more recent respect for bayesian methods. Note the years in parenthesis is not to say that nothing new has been used from those areas, more like if you pick up a book on that topic from that year you would be pretty well covered for the purposes of ML. Worth having a vague idea of are stuff like PAC learning, topology and computational complexity stuff like Valiant's work on evolvability. If you are doing stuff related to genetic programming then category and type theory have riches to be plundered. Or if you want to be more hardcore and are looking at very higher dimensional data and reductions on them you might look at algebraic geometry (in particular algebraic varieties) and group theory. So basically the answer to your question is as little or as much math as you want and or depending on the problem and your interests in trying different approaches than the typical toolkits of linear algebra and statistics.
- nkh 15y ago
  * If you are doing stuff related to genetic programming then category and type theory have riches to be plundered.* Could you expand this a bit as I don't understand the meaning. Are you saying that if the problem you are working on can be solved with genetic algorithms, then you could blow it away with category and type theory? I don't have a vested interest in either, I am just curious. Thanks.
  - o1iver 15y ago
    He said genetic programming not genetic algorithms. For the former, type and category theory are useful!

What kind of math skills? What would a programmer need to learn in order to work on ML stuff?

by suneilp 15y ago

What kind of math skills? What would a programmer need to learn in order to work on ML stuff?

Aside from algebra to do log or exp division addition and multiplications, you have to be versed in statistics. Most ml problems are solved on statistical bases. Although many of the algorithms have been solved, you still need to grasp the statistics behind it which is a bit more involved than calculating the odds of a die

chaostheory 15y ago
Yes, many people that I know working on ML do not remember statistics well enough. Some keep their ignorance while relying on mathematicians on-staff (who can't really code), while others either start buying college textbooks or go to night classes. There are too many people who don't understand the algorithms being used.

Dn_Ab 15y ago

To work with ML stuff you don't need much. You can just download packages and get experience with how to best pick models, choose features and tweak (hyper)parameters. If you want to work on or understand it then you will need math. You need a decent understanding of calculus (mid 1800s level, mulitvariate calculus), a more decent understanding of Linear Algebra (1950s ), information theory (1960s), and probability and statistics. With the last having shifted the most from the past due to more recent respect for bayesian methods. Note the years in parenthesis is not to say that nothing new has been used from those areas, more like if you pick up a book on that topic from that year you would be pretty well covered for the purposes of ML. Worth having a vague idea of are stuff like PAC learning, topology and computational complexity stuff like Valiant's work on evolvability. If you are doing stuff related to genetic programming then category and type theory have riches to be plundered. Or if you want to be more hardcore and are looking at very higher dimensional data and reductions on them you might look at algebraic geometry (in particular algebraic varieties) and group theory. So basically the answer to your question is as little or as much math as you want and or depending on the problem and your interests in trying different approaches than the typical toolkits of linear algebra and statistics.

nkh 15y ago
* If you are doing stuff related to genetic programming then category and type theory have riches to be plundered.* Could you expand this a bit as I don't understand the meaning. Are you saying that if the problem you are working on can be solved with genetic algorithms, then you could blow it away with category and type theory? I don't have a vested interest in either, I am just curious. Thanks.
- o1iver 15y ago
  He said genetic programming not genetic algorithms. For the former, type and category theory are useful!