Ask HN: How to get started with machine learning?

That is absolute nonsense.

There is clear correlation between gender and survival rates. Given the data, a decent prior would absolutely take that into account.

Yes, there are other factors. But the foundation of statistical models is simplification, and descriptive statistics are an important foundation of that.

In any case, it isn't exactly clear that there are magical hidden factors which predicted survival. It appears you maybe unfamiliar with the event, because basically those who got into a lifeboat survived, and those who didn't, didn't survive.

To quote Wikipedia:

Almost all those who jumped or fell into the water drowned within minutes due to the effects of hypothermia.... The disaster caused widespread outrage over the lack of lifeboats, lax regulations, and the unequal treatment of the three passenger classes during the evacuation..... The thoroughness of the muster was heavily dependent on the class of the passengers; the first-class stewards were in charge of only a few cabins, while those responsible for the second- and third-class passengers had to manage large numbers of people. The first-class stewards provided hands-on assistance, helping their charges to get dressed and bringing them out onto the deck. With far more people to deal with, the second- and third-class stewards mostly confined their efforts to throwing open doors and telling passengers to put on lifebelts and come up top. In third class, passengers were largely left to their own devices after being informed of the need to come on deck.

Even more tellingly:

The two officers interpreted the "women and children" evacuation order differently; Murdoch took it to mean women and children first, while Lightoller took it to mean women and children only. Lightoller lowered lifeboats with empty seats if there were no women and children waiting to board, while Murdoch allowed a limited number of men to board if all the nearby women and children had embarked

All this behavior matches exactly what the model tells us about the event.

I'd be very interested if you can point to something specific that is wrong about it.

All models are wrong, but some are useful.

Thanks!

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/articles/PMC1291331/

IMO the best way to get started (like with anything) is by getting started. I think the way you make progress is going to come down to you personally as an individual and what your motivations are. Before learning ANYTHING new i would invest some time in learning how to learn. There is a good coursera course on this https://www.coursera.org/learn/learning-how-to-learn and the book by the course authors is incredibly useful for putting a framework with some techniques that can help the approach to learning any new skill. This is not meant to be condescending advice but for me personally it's changed the way i go about learning any new skill now.

I think as well it really depends where you are coming from / what your background is. The reason i say this is i have recently gone through a similar transition into machine learning 'from scratch' except once i got there i realised i knew more than i thought. My academic background is in psychology / biomedical science which involved a LOT of statistics. From my perspective once i started getting into the field i realised there are a lot of things i already knew from stats with different terms in ML. It was also quite inspiring to see many of the eminent ML guys have backgrounds in Psychology (for instance Hinton) meaning i felt perhaps a bit more of an advantage on the theoretical side that many of my programming peers don't have.

I realise most people entering the field right now have a programming background so will be coming at things from an opposite angle. For me i find understanding the vast majority of the tests and data manipulation pretty standard undergraduate stuff (using python / SK Learn is incredible because the library does so much of the heavy lifting for you!). Where i have been struggling is in things that an average programmer probably finds very basic - it took me 3 days to get my development environment set up before i could even start coding (solved by Anaconda - great tool and lessons learned). Iterating over dictionaries = an nightmare for me (at first anyway, again getting better).

I think (though i may be biased) it's easier to go from programming to ML rather than the other way around because so much of ML is contingent on having decent programming skills. If you have a decent programming skill set you can almost 'avoid' the math component in a sense due to the libraries available and support online. There are some real pluses to ML compared to traditional statistics - i.e. tests that are normally ran in stats to check you are able to apply the test (i.e. shape of the data: skewness / kurtosis, multicollinearity etc) become less of an issue as the algorythms role is to deliver an output given the input.

I would still recommend some reading into the stats side of things to get a sense of how data can be manipulated to give different results because i think this will give you a more intuitive feel for parameter tuning.

This book does not look very relevant but it's actually a really useful introduction to thinking about data and where the numbers we hear about actually come from

https://www.amazon.co.uk/Risk-Savvy-Make-Good-Decisions/dp/1...

In conclusion if you can programme and have a good attitude towards learning and are diligent with efforts I think this should be a simple transition for you.

I think you are making the point for him. If you look at the predictive models people make on these, they make a big deal about your sex and status being the main indicators of who survived. The reality is that the main causal indicator for survival was access to a lifeboat.

Now, it so happens that that correlated heavily with class. But, not as much as with sex. Though, there were some places where being male hurt your chances (as you point out in the one officer not allowing men on boats), by and large these were secondary and correlated with success, not predictors of it.