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> The key difference is whether the candidate truly understands the mathematical and statistical basis of machine learning Can you elaborate on this, and at wh

by runT1ME 10y ago

> The key difference is whether the candidate truly understands the mathematical and statistical basis of machine learning Can you elaborate on this, and at what level? Are you talking about a PhD level of understanding of cutting edge mathematics, or do you mean understand the basics, or somewhere in between?

hardtke 10y ago
Somewhere in between. A simple question I use is "how do I decide whether to add a feature to a classification model?" Most candidate are fine until I bring up the topic of correlated features.
- DrNuke 10y ago
  Are you hiring for finance? In Europe it still depends very much on the industry: some mean coding, some maths, some infrastructure, all want data preparation and in-depth stats. edit: other than understanding the business fundamentals, of course.
- akvadrako 10y ago
  Do you mean like non-orthogonal dimensions?
- photoJ 10y ago
  That's terrible.
shas3 10y ago
I think the following interview questions can help screen candidates (not an exhaustive list): 1. Asking a candidate to cast a non-standard problem of classification or estimation into a tractable optimization problem. (this is a very valuable skill that someone who has done good studies in numerical linear algebra/machine learning/stat/information theory/control systems/signal processing/math/etc. should be able to do) 2. Asking them to take an algorithm they have used and explain every step in deriving the algorithm. (It will help interviewer calibrate the level of learning in the interviewee. Also helps screen for indisciplined black-box users.) 3. Presenting challenging machine learning scenarios: using customized ensemble learning approaches, imbalanced data sets, noisy labels, multiple instances, different error metrics, etc. and seeing how interviewee approaches the problem from first principles (real world problems almost always involve some of these issues) 4. Testing their intuitions in "feature-engineering" for different types of data. (with the partial exception of cases where rigorous research/successful products show the utility of deep learning, one has to almost necessarily do a fair bit of feature-engineering)
- ice109 10y ago
  very good response
plafl 10y ago
I usually ask about what the candidate has worked on. Or maybe the topic of their PhD thesis. I prefer that people explain to me what they know instead of having a list of questions about what I know.

by runT1ME 10y ago

hardtke 10y ago

Somewhere in between. A simple question I use is "how do I decide whether to add a feature to a classification model?" Most candidate are fine until I bring up the topic of correlated features.

DrNuke 10y ago
Are you hiring for finance? In Europe it still depends very much on the industry: some mean coding, some maths, some infrastructure, all want data preparation and in-depth stats. edit: other than understanding the business fundamentals, of course.
akvadrako 10y ago
Do you mean like non-orthogonal dimensions?
photoJ 10y ago
That's terrible.

shas3 10y ago

I think the following interview questions can help screen candidates (not an exhaustive list): 1. Asking a candidate to cast a non-standard problem of classification or estimation into a tractable optimization problem. (this is a very valuable skill that someone who has done good studies in numerical linear algebra/machine learning/stat/information theory/control systems/signal processing/math/etc. should be able to do) 2. Asking them to take an algorithm they have used and explain every step in deriving the algorithm. (It will help interviewer calibrate the level of learning in the interviewee. Also helps screen for indisciplined black-box users.) 3. Presenting challenging machine learning scenarios: using customized ensemble learning approaches, imbalanced data sets, noisy labels, multiple instances, different error metrics, etc. and seeing how interviewee approaches the problem from first principles (real world problems almost always involve some of these issues) 4. Testing their intuitions in "feature-engineering" for different types of data. (with the partial exception of cases where rigorous research/successful products show the utility of deep learning, one has to almost necessarily do a fair bit of feature-engineering)

ice109 10y ago
very good response

plafl 10y ago

I usually ask about what the candidate has worked on. Or maybe the topic of their PhD thesis. I prefer that people explain to me what they know instead of having a list of questions about what I know.