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Feynman is a notable exception to the trend. Using a single exceptional result as evidence for decrying a trend is foolish.

You missed my question. I am concerned with how many smart people with disabilities IQ exams misclassifies. I am not using Feynman to say he had a disability or anything, I was using him as an example of a case where his IQ score doesn't match his actual intelligence, and I am in no way claiming why that may be. I am merely stating IQ exams aren't perfect. If you are interested in replying, then reply to the question I posed. I think IQ test potentially misclassifies people with high functioning autism and other highly intelligent people with mental disabilities, showing they have lower intelligence than they actually have.

Your comment doesn’t address my question of how many people are misclassified by IQ test. I suspect smart people with high functioning autism and the like are not accurately measured. Not saying any of this applies to Feynman by the way. Just used him as an example of where an IQ exam underestimated someone.

I wonder how many IQ test misclassify intelligence? Feynman had a score of 125 and many people here have a high score than that. I bet no one here with a higher score of 125 are actually smarter than Feynman though. I think this is just one of many possible such examples where IQ test miss the purported people they were suppose to capture. Feynman’s score wouldn’t even qualify him for MENSA.

I wonder how many IQ exams misclassify the intelligence of smart people with disabilities of some sort or high functioning autism. Not saying any of this applies to Feynman, he is just an example of where the test didn’t accurately capture his intellect.

Sounds like you went to a good high school. Mine was a terrible high school that offered 0 computer science classes.

One really intelligent decision the department made was to not just mimic a college curriculum.

It would have been GREAT if my high school even mimicked a college curriculum. We barely had any AP courses & I think it would haven't been a surprised if you didn't go onto college from my high school. You re definitely speaking from a place of immense privilege and it's amazing your school offered that.

Thanks. Can you list what courses one should focus on as a baseline to do research in ML? You mentioned Probability theory & Linear algebra, but what specific topics in those areas should one learn? Any other fields of math?

I have several questions regarding this:

1) Do FAANG companies hire non-PhDs for machine learning positions? Most seem to require a MS or PhD

2) What are the interview questions like at FAANG companies for machine learning positions? Is the interview different if you don't have a PhD?

3) For non-PhDs applying, what are the math requirements for the job?

4) For people that have a PhD working in ML at a FAANG, do you feel like you use your PhD level skills day-to-day?

Every few months, I check that all our recommended resources are still up-to-date, available, and free.

What are your recommended resources for technical improvements in coding interviews?

Is this why these companies have such intensive leet coding interview processes? In other professions a degree + exams alone is enough.

I can't imagine an interview where a doctor has to perform surgery "as a take home interview" without pay or be able to solve obscure problems from chemistry, biology, etc.

The first is that self-driving is super difficult, they don't really have the expertise

What about all of the talent they hired from CMU?

I don't see a way to respond to your latest reply, but thank you for the recommendations! I'll take a look at them.

Thanks for the context. That sounds like a good plan to me regarding post-doc locations. I am also interested in theory side of ML. What areas of mathematics should one learn really well that apply to the theory side? What blogs, papers, books would you point one to to learn the theory side more? To your knowledge are their applications of abstract algebra to ML? If so, what areas of algebra apply & what problems do they solve?

I am considering going into a CS PhD focusing in ML. The mid 40k-70k range was from quick google search I did for CS post docs in ML in lower cost of living areas where the cost of living is much lower than on the West Coast. I am trying to look at career prospects and weigh whether it makes sense to stay in academia or jump to industry (after I complete a PhD). If wages are closer to 60k-100k for post docs, then I may consider staying in academia for some time after completing a PhD depending on whether my career interest shift.

I am primarily interested in the experience of ML post-docs that forgo going into industry. Can you elaborate a bit more about their experience? Salaries look a bit lower for post docs vs industry work. Some I've seen start in the mid-40k to 70k range. Could be different based on geography.

Thats why some companies stop hiring ppl after 30s

This is a huge generalization. Some people's careers start in their 30s. Would you think some 30s something ML researcher that just got a PhD is over the hill? Not everyone is married and has kids in their 30s either.

Can anyone speak to what it's like doing a post doc in CS?

But they don't have as much experience programming?

Then by that metric you don't even need a degree and your initial point of a BS > Masters > Bootcamp is nullified by the self-taught programmer that has programmed longer than anyone else in that group that happens to apply for your position. But that doesn't necessarily mean they will be a better programmer in the long term.

In my experience, bachelor's in CS counts more than master's in CS

This discounts people that transition to CS from another field like mathematics, chemistry, physics, etc. Just because you didn't major in CS as an undergraduate doesn't mean you can't program. That is a bit of gatekeeping. Plus, I'd argue someone from a field like mathematics and physics might be more suited to do certain types of programming jobs due to their background skillsets.

Do Data Engineering jobs require you to have knowledge of statistics, machine learning, etc? Or can you get data engineering jobs where you are focused on connecting pipelines and data flow? I am interested in a data engineering job, but it appears a lot of data engineering jobs require machine learning expertise in job listings. I am interested in just a data engineering focused role.