Row 84460

Row ID: 84460 | Dataset Entry | Axioma AXP Content Repository

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In my experience of interviewing candidates for ML Engineering and Research positions, those with strictly Math (even PhD level) backgrounds would tend to overcomplicate basic ML concepts and struggled to organize their thoughts in terms of both i) applying models to real-world problems and ii) coming up with solutions to the types of hurdles one typically faces when deploying and using ML/Deep Learning in an engineering context. For this reason, I believe at some points the mathematics stops returning on investment, so to speak. You certainly need very strong *undergraduate* mathematics in Linear Algebra, Probability/Statistics, and Multivariable Calculus, but the abstract mathematical theory of each of those is not as helpful in a ML Engineering context. For example: i) The abstract theory of linear algebra in terms of dual spaces is insanely useful for those interested in Wavelets and other approximation theoretic fields but aren't necessary if you are trying to use such transforms in a model. ii) In Probability, the theory of continuous-time Martingales can be used to derive generalization bounds in supervised (I believe, don't quote me on this) and [online learning](https://www.mit.edu/~rakhlin/papers/chervonenkis_chapter.pdf), but it's not very common you'll be using a Martingale in some model you are building in industry. iii) In multivariable analysis, being able to understand parameterized surfaces, gradients/Jacobians, change of variables in high-dimensions are all very useful, but the abstract view of multi-dimensional integration in terms of differential forms isn't nearly as useful in industry.

To me the most useful yet more mathematically (relatively) intense subjects that are relevant in industry are Bayesian statistics and convex optimization.

FieldValue
text In my experience of interviewing candidates for ML Engineering and Research positions, those with strictly Math (even PhD level) backgrounds would tend to overcomplicate basic ML concepts and struggled to organize their thoughts in terms of both i) applying models to real-world problems and ii) coming up with solutions to the types of hurdles one typically faces when deploying and using ML/Deep Learning in an engineering context. For this reason, I believe at some points the mathematics stops re…
label r/machinelearning
dataType comment
communityName r/MachineLearning
datetime 2024-05-24
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Raw Record

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  "text": "In my experience of interviewing candidates for ML Engineering and Research positions, those with strictly Math (even PhD level) backgrounds would tend to overcomplicate basic ML concepts and struggled to organize their thoughts in terms of both i) applying models to real-world problems and ii) coming up with solutions to the types of hurdles one typically faces when deploying and using ML/Deep Learning in an engineering context. For this reason, I believe at some points the mathematics stops returning on investment, so to speak.  \n  \nYou certainly need very strong *undergraduate* mathematics in Linear Algebra, Probability/Statistics, and Multivariable Calculus, but the abstract mathematical theory of each of those is not as helpful in a ML Engineering context. For example:   \ni) The abstract theory of linear algebra in terms of dual spaces is insanely useful for those interested in Wavelets and other approximation theoretic fields but aren't necessary if you are trying to use such transforms in a model.   \nii) In Probability, the theory of continuous-time Martingales can be used to derive generalization bounds in supervised (I believe, don't quote me on this) and [online learning](https://www.mit.edu/~rakhlin/papers/chervonenkis_chapter.pdf), but it's not very common you'll be using a Martingale in some model you are building in industry.   \niii) In multivariable analysis, being able to understand parameterized surfaces, gradients/Jacobians, change of variables in high-dimensions are all very useful, but the abstract view of multi-dimensional integration in terms of differential forms isn't nearly as useful in industry.\n\nTo me the most useful yet more mathematically (relatively) intense subjects that are relevant in industry are Bayesian statistics and convex optimization.",
  "label": "r/machinelearning",
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  "datetime": "2024-05-24",
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