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Ask HN: How to get started with machine learning?

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Re: Ask HN: How to get started with machine learning?

#111

Post by taeric »

BickNowstrom wrote: post #89
dschiptsov wrote: post #83

Earlier quoted context omitted.

Correlations does not imply causation. There were many more relevant but "invisible" variables, which, probably, related to some genetic factors, like ability to sustain exposure to the cold water, ability to calm oneself down to avoid panic and self-control in general, strong survival instinct to literally fight the others, etc. The variables you have described, except the age of a passenger, are visible but irrelev…

This challenge is not about causal inference. I do agree it is more of a toy dataset, to get started with the basics, and that there are a lot of other variables that go into survivability. But to say these variables, except for age, are irrelevant is mathematically unsound: You can show with cross-validation and test set performance that your model using these variables generalizes (around 0.80 ROC AUC). You can do…

How does this not violate [1]? That is, this seems specifically anti-statistical. The best you can come up with on this is a predictive model that you then have to test on new events. In this case, that would likely mean new crashes.

[1] https://en.wikipedia.org/wiki/Testing_hypotheses_suggested_b...

Re: Ask HN: How to get started with machine learning?

#112

Post by krosaen »

zump wrote: post #101
krosaen wrote: post #60

I took the summer off to learn enough ML to transition from a career in software engineering & product / leadership type roles to ML. I suggest for a first round learning practical tools and techniques so you can start applying supervised learning techniques right away while also starting to build a more solid foundation in probability & statistics for future deeper understanding of the field. I've written about my c…

So, any luck getting a job? Not sure whether I should do this too.

Yeah, I'm recently started as a research engineer in a lab at University of Michigan doing self-driving car stuff, will update the website with more info and post-summer reflections within a couple weeks.

Re: Ask HN: How to get started with machine learning?

#113

Post by zump »

krosaen wrote: post #112
zump wrote: post #101

Earlier quoted context omitted.

So, any luck getting a job? Not sure whether I should do this too.

Yeah, I'm recently started as a research engineer in a lab at University of Michigan doing self-driving car stuff, will update the website with more info and post-summer reflections within a couple weeks.

Get that job thru connections or a public job listing when you're competing against other applicants?

That's the key question.

Re: Ask HN: How to get started with machine learning?

#114

Post by krosaen »

zump wrote: post #113
krosaen wrote: post #112

Earlier quoted context omitted.

Yeah, I'm recently started as a research engineer in a lab at University of Michigan doing self-driving car stuff, will update the website with more info and post-summer reflections within a couple weeks.

Get that job thru connections or a public job listing when you're competing against other applicants? That's the key question.

Ha, well it was a publicly listed job, but I got pointed to it and eventually introduced to the profs in the course of networking with ML folks in town. I can't speak to how many applicants.

Re: Ask HN: How to get started with machine learning?

#115

Post by YeGoblynQueenne »

nl wrote: post #106
dschiptsov wrote: post #103

Earlier quoted context omitted.

> All models are wrong, but some are useful. Exactly. All predictions are wrong and make no sense for partially observable, multiple causation, mostly stochastic phenomena. It will never be the same.

Except that this model was useful. The Titanic's sister ship (the Brittanic) was torpedoed during WW1 and sunk. However, the lesson of the Titanic (too few lifeboats) had been learnt, and only 26 people died. I don't know what point you are trying to make - yes, I agree that history never repeats, but lessons can be learnt from it, and they can be quantified and they can be useful. https://www.ncbi.nlm.nih.gov/pmc/ar…

>> The Titanic's sister ship (the Brittanic) was torpedoed during WW1 and sunk. However, the lesson of the Titanic (too few lifeboats) had been learnt, and only 26 people died.

This happened because they made a _statistical_ model of the Titanic disaster, and learned from it? Like, they actually crunched the numbers and plotted a few curves etc, and then said "aha, we need more boats"?

I kind of doubt it, and if it wasn't the case then you can't very well talk about a "model", in this context. It's more like they had a theory of what factor most heavily affected survival and acted on it. But I'd be really surprised to find statistics played any role in this.

Re: Ask HN: How to get started with machine learning?

#116

Post by YeGoblynQueenne »

BickNowstrom wrote: post #89
dschiptsov wrote: post #83

Earlier quoted context omitted.

Correlations does not imply causation. There were many more relevant but "invisible" variables, which, probably, related to some genetic factors, like ability to sustain exposure to the cold water, ability to calm oneself down to avoid panic and self-control in general, strong survival instinct to literally fight the others, etc. The variables you have described, except the age of a passenger, are visible but irrelev…

This challenge is not about causal inference. I do agree it is more of a toy dataset, to get started with the basics, and that there are a lot of other variables that go into survivability. But to say these variables, except for age, are irrelevant is mathematically unsound: You can show with cross-validation and test set performance that your model using these variables generalizes (around 0.80 ROC AUC). You can do…

>> Domain experts get beaten on Kaggle, because they think they need other variables, or that some variables (and their interactions) can't possibly work.

That sounds a bit iffy. A domain expert should really know what they're talking about, or they're not a domain expert. If the real deal gets beaten on Kaggle it must mean that Kaggle is wrong, not the domain expert.

Not that domain experts are infallible, but if it's a systematic occurrence then the problem is with the data used on Kaggle, not with the knowledge of the experts.

I mean, the whole point of scientific training and research is to have domain experts who know their shit, know what I mean?

Re: Ask HN: How to get started with machine learning?

#117

Post by YeGoblynQueenne »

By all means get some "background knowledge" (linear algebra, statistics, calculus etc), play around with libraries and follow some MOOC, but primarily I'd suggest you go get yourself a post-graduate degree from a brick-and-mortar university, and in a course called "Data Science" or "Artificial Intelligence" and the like.

You can learn on your own, of course, but a university course will focus your learning, provide rich feedback, and give you a strong foundation on which to build. You'll also get to learn from other students, which is not often the case in MOOCs. And there's nothing like having a teacher on your payroll (which is essentially what paying for a course is) to answer your questions, clarify obscure areas in books and generally support you throughout the course.

For the record- I did exactly what I say above. After five years working in the industry as a dev, I took a Masters part-time, sponsored by my employer. I think I got a good foundation as I say above, and I certainly didn't have the time, or the focus, to learn the same things on my own.

And I did try on my own, with MOOCs-and-books for a while. I did learn useful stuff (the introductory AI course from Udacity for instance, was really helpful) but after starting the Masters it felt like all this time I'd been crawling along without aim, and now I was running.

Re: Ask HN: How to get started with machine learning?

#118

Post by nl »

YeGoblynQueenne wrote: post #115
nl wrote: post #106

Earlier quoted context omitted.

Except that this model was useful. The Titanic's sister ship (the Brittanic) was torpedoed during WW1 and sunk. However, the lesson of the Titanic (too few lifeboats) had been learnt, and only 26 people died. I don't know what point you are trying to make - yes, I agree that history never repeats, but lessons can be learnt from it, and they can be quantified and they can be useful. https://www.ncbi.nlm.nih.gov/pmc/ar…

>> The Titanic's sister ship (the Brittanic) was torpedoed during WW1 and sunk. However, the lesson of the Titanic (too few lifeboats) had been learnt, and only 26 people died. This happened because they made a _statistical_ model of the Titanic disaster, and learned from it? Like, they actually crunched the numbers and plotted a few curves etc, and then said "aha, we need more boats"? I kind of doubt it, and if it w…

This happened because they made a _statistical_ model of the Titanic disaster, and learned from it?

No - statistics as the discipline that we think of today wasn't really around until the work of Gosset[1] and Fisher[2] which was done a few years after this.

I'm sure you noted that I was very careful with what I claimed: "the lesson of the Titanic (too few lifeboats) had been learnt".

These days we'd quantify the lesson with statistics. Then, they didn't have that tool.

Instead, we have testimony[3] relaying the same story: Just one question. Have you any notion as to which class the majority of passengers in your boat belonged? - (A.) I think they belonged mostly to the third or second. I could not recognise them when I saw them in the first class, and I should have known them if there were any prominent people. (Q.) Most of them were in the boat when you came along? - (A.) No. (Q.) You put them in? - (A.) No. Mr. Ismay tried to walk round and get a lot of women to come to our boat. He took them across to the starboard side then - our boat was standing - I stood by my boat a good ten minutes or a quarter of an hour. (Q.) At that time did the women display a disinclination to enter the boat? - (A.) Yes."

So yes, I agree - it was a theory, which our modern modelling tools can show matched well with what the statistics showed happened.

My whole point is that this is very useful, unlike the OP who dismissed it as useless.

[1] https://en.wikipedia.org/wiki/William_Sealy_Gosset

[2] https://en.wikipedia.org/wiki/Ronald_Fisher

[3] http://www.titanicinquiry.org/BOTInq/BOTInq33Header.php

Re: Ask HN: How to get started with machine learning?

#119

Post by [deleted] »

YeGoblynQueenne wrote: post #116
BickNowstrom wrote: post #89

Earlier quoted context omitted.

This challenge is not about causal inference. I do agree it is more of a toy dataset, to get started with the basics, and that there are a lot of other variables that go into survivability. But to say these variables, except for age, are irrelevant is mathematically unsound: You can show with cross-validation and test set performance that your model using these variables generalizes (around 0.80 ROC AUC). You can do…

>> Domain experts get beaten on Kaggle, because they think they need other variables, or that some variables (and their interactions) can't possibly work. That sounds a bit iffy. A domain expert should really know what they're talking about, or they're not a domain expert. If the real deal gets beaten on Kaggle it must mean that Kaggle is wrong, not the domain expert. Not that domain experts are infallible, but if it…

[deleted]

Re: Ask HN: How to get started with machine learning?

#120

Post by BickNowstrom »

taeric wrote: post #111
BickNowstrom wrote: post #89

Earlier quoted context omitted.

This challenge is not about causal inference. I do agree it is more of a toy dataset, to get started with the basics, and that there are a lot of other variables that go into survivability. But to say these variables, except for age, are irrelevant is mathematically unsound: You can show with cross-validation and test set performance that your model using these variables generalizes (around 0.80 ROC AUC). You can do…

How does this not violate [1]? That is, this seems specifically anti-statistical. The best you can come up with on this is a predictive model that you then have to test on new events. In this case, that would likely mean new crashes. [1] https://en.wikipedia.org/wiki/Testing_hypotheses_suggested_b...

Because we are not doing hypothesis testing, we are doing classification on a toy dataset. Sure, one could treat this as a forecasting challenge, but then one would need another Titanic sinking in roughly the same context, with the same features... That demand is as unreasonable as calling this modeling knowledge competition meaningless.

And if you see classification as a form of hypothesis testing, then cross-validation is a valid way of testing if hypothesis holds on unseen data.

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