joker3

Except Mama Cass.

Finally I can take NumPy seriously now that it's been published.

Peter and his former student Dan McCarthy have done a lot of work on CLV calculations.

The output of a machine learning algorithm is code that no one was clever enough to write. In light of Kernighan's law, that should give you pause.

Is there ever a right time to relax meat safety rules?

NEXP is nondeterministic exponential time, right? In that case we know that P /= EXPTIME by the time hierarchy theorem and EXPTIME is contained in NEXP. We also have a direct result that NP /= NEXP (reference given in https://complexityzoo.uwaterloo.ca/Complexity_Zoo:N#nexp).

Is there a non-paywalled version?

Probably in 2030 we're going to see a similar but smaller hit to those sorts of schools. What effect that has is anyone's guess right now.

You think it's bad now? Wait until you see what happens in 2026.

As many of you remember, the bottom dropped out of the US economy in 2008. As a result, many people delayed having children for a few years (or just decided not to). That means that the freshman class of 2026 is going to be significantly smaller than that of 2025, and that's going to shut down a large number of small and financially strapped colleges.

It's difficult to imagine exactly what's going to happen afterwards, but we can be confident that things will be different.

Not everyone on HN knows about C++11 threading. A "hello world" is of interest to some people.

Concentration of measure. If you have a quantity that depends on a large number of random variables but not too strongly on any small subset of them, it tends to behave like a constant. That's the intuition behind the law of large numbers, the central limit theorem, a bunch of concentration inequalities, and model averaging.

Take a look at https://ocobook.cs.princeton.edu/OCObook.pdf, and in particular section 1.2. The online shortest path problem is a good example of an online optimization problem that's not related to machine learning.

This is nice to have. The classic reference on online learning is "Prediction, Learning and Games" but it's not ML focused and it predates pretty much everything on online convex optimization. I'm looking forward to seeing a more modern introduction.

1) Human height isn't actually bimodal even if some histograms display two modes. See https://amstat.tandfonline.com/doi/abs/10.1198/00031300265#.... for an analysis.

2) The central limit theorem applies to the distribution of the sample average. It applies whenever the samples are iid and the second moment is finite. The fact that the samples are coming from a mixture of normals doesn't change that.

A useful more advanced tip: don't put in writing that you're taking the conversation offline to avoid putting securities fraud in writing.

I had a terrible time in academia and I'll agree with everything that the OP wrote.

Not everyone starts a PhD right after undergrad. It'd be interesting to look at PhD students who started later in life and see how they take the program. Anecdotally, they have a lot of problems too.

The author of this article is a researcher in cognitive psychology specializing in education. He wrote a pop-sci book detailing how cognitive science explains what's wrong with school, and it's very much worth a read if you're at all interested in the topic. Link is https://www.amazon.com/Why-Dont-Students-Like-School/dp/0470....

And counties with the lowest incidence of kidney cancer are small.

In mice.

Well, you get the idea.

Or to put it more succinctly, life is path-dependent.

This looks like it's roughly at the level of Allan Gut's intermediate probability book (https://www.amazon.com/Intermediate-Course-Probability-Sprin...). There's a need for more exposition at that level, but I'm not sure that the course notes themselves are a good source.

The database course I took covered that a bit. We used https://www.amazon.com/Database-Management-Systems-Raghu-Ram.... It's not the most up-to-date text (although it was when I was in school), but it's probably still the best source for the basics of relational database theory and implementation.

The procedure is mechanical, but understanding why it works is quite a bit harder.

A lot of machine learning people don't really understand study design or power or things like that. It's gotten a little better over the past decade or so, but this is an area where the field has a lot of room to improve.

Why do we need to be able to add two characters again?

The introduction is always the last thing you write. Presumably Jean's not done yet.

Rscript can also be used to run R scripts from the command line. Just put >#!/usr/bin/env Rscript on the first line.

Do you know a good introduction to adaptive/sequential designs? I'm looking for something along the lines of a textbook aimed at a graduate level seminar.

I'm just going to present the absolute basics of the topic. It'll be a high-level overview of something along the lines of chapters 1-4 of https://www.amazon.com/Analysis-Experiments-Chapman-Statisti....