This is a major piece of my machine learning self study curriculum:
Some links to problem set solutions there
All of Statistics, by Larry Wassserman (2013) [pdf]
31–40 of 55 posts
Re: All of Statistics, by Larry Wassserman (2013) [pdf]
#32Earlier quoted context omitted.
Suggestions for a more rigorous treatment?
Anything with measure theory, though you will regret it :)
stochastic calculus really hurt me. i did well for it, I TA'd for it, i look at it from time to time in my work, but I still find it very difficult. i don't think i'm much good at mathematics, i just try very hard and hope for the best.
Re: All of Statistics, by Larry Wassserman (2013) [pdf]
#33Happy to see a book like this trending on hn, especially with a sentence like: "Using fancy tools like neural nets, boosting, and support vector machines without understanding basic statistics is like doing brain surgery before knowing how to use a band-aid." in it's preface. I definitely agree, since I wasted a lot of time doing fruitless surgeries before I went and learned about band aids in depth. From my look at…
> "Using fancy tools like neural nets, boosting, and support vector machines without understanding basic statistics is like doing brain surgery before knowing how to use a band-aid."
Having studied both statistics and neural networks, I'm not sure if I completely agree with that quote. There are lots of neural network applications that have little to do with statistics (image recognition with convolutional neural networks for example).
I am pretty sure that the author means neural networks for statistical applications though.
Re: All of Statistics, by Larry Wassserman (2013) [pdf]
#34Happy to see a book like this trending on hn, especially with a sentence like: "Using fancy tools like neural nets, boosting, and support vector machines without understanding basic statistics is like doing brain surgery before knowing how to use a band-aid." in it's preface. I definitely agree, since I wasted a lot of time doing fruitless surgeries before I went and learned about band aids in depth. From my look at…
> "Using fancy tools like neural nets, boosting, and support vector machines without understanding basic statistics is like doing brain surgery before knowing how to use a band-aid." Having studied both statistics and neural networks, I'm not sure if I completely agree with that quote. There are lots of neural network applications that have little to do with statistics (image recognition with convolutional neural net…
Image classification has everything to do with statistics: you're guessing the probability distribution over the classes conditional on the input image vector; the model is trained through a process of statistical inference (using gradient descent).
Re: All of Statistics, by Larry Wassserman (2013) [pdf]
#35Happy to see a book like this trending on hn, especially with a sentence like: "Using fancy tools like neural nets, boosting, and support vector machines without understanding basic statistics is like doing brain surgery before knowing how to use a band-aid." in it's preface. I definitely agree, since I wasted a lot of time doing fruitless surgeries before I went and learned about band aids in depth. From my look at…
Suggestions for a more rigorous treatment?
[deleted]
Re: All of Statistics, by Larry Wassserman (2013) [pdf]
#36Earlier quoted context omitted.
Suggestions for a more rigorous treatment?
Anything with measure theory, though you will regret it :)
On the probability side, I thought Ash's "Probability & Measure Theory" was good for self-study, although some experience with real analysis is definitely a prerequisite. It can be pretty dense at times, but well worth going through.
Re: All of Statistics, by Larry Wassserman (2013) [pdf]
#37Sadly, no link to free eBook, which is not surprising because it seems that the book is still in print, having been released as recently as 2004, and updated in 2005 and 2013. This post links to the website supporting the book and provides links to errata, code and data. The links on the page to Springer and Amazon are broken: Here are valid links: http://www.springer.com/de/book/9780387402727 http://www.amazon.com/A…
Not sure about HN's policy on posting links to pirated material, but as a Freedom of Information supporter, I will note that the book can be found at http://gen.lib.rus.ec .
Direct link [pdf] [9mb] http://libgen.io/get/B9E6052395BA047BD154EA45130026FC/Larry%...
Re: All of Statistics, by Larry Wassserman (2013) [pdf]
#38Happy to see a book like this trending on hn, especially with a sentence like: "Using fancy tools like neural nets, boosting, and support vector machines without understanding basic statistics is like doing brain surgery before knowing how to use a band-aid." in it's preface. I definitely agree, since I wasted a lot of time doing fruitless surgeries before I went and learned about band aids in depth. From my look at…
> "Using fancy tools like neural nets, boosting, and support vector machines without understanding basic statistics is like doing brain surgery before knowing how to use a band-aid." Having studied both statistics and neural networks, I'm not sure if I completely agree with that quote. There are lots of neural network applications that have little to do with statistics (image recognition with convolutional neural net…
>There are lots of neural network applications that have little to do with statistics (image recognition with convolutional neural networks for example).
You're kidding, right? The most fundamental reasons that deep convnets work at all are statistical in nature.
Re: All of Statistics, by Larry Wassserman (2013) [pdf]
#39Earlier quoted context omitted.
As an EE, how would you explain concepts like a PN junction or field effect transistor without using statistical mechanics? (Ie, expected behaviour for ensembles of huge numbers of particles).
The models EE use are simplified, drift and diffusion current and electron and holes with their different mobilities and energy levels. Math apparatus used here, and strictly related to statistics, is limited to averaging, I would dare to say.
Quantum Monte Carlo simulation is pretty standard for modern semiconductor devices. Most of the effects of interest in highly scaled transistors, for example, cannot be properly accounted for otherwise.
On the basic materials level, density functional theory is the current gold standard, and it's extremely statistics heavy.
At the systems and architecture levels, you may be right, though.
Re: All of Statistics, by Larry Wassserman (2013) [pdf]
#40Earlier quoted context omitted.
> "Using fancy tools like neural nets, boosting, and support vector machines without understanding basic statistics is like doing brain surgery before knowing how to use a band-aid." Having studied both statistics and neural networks, I'm not sure if I completely agree with that quote. There are lots of neural network applications that have little to do with statistics (image recognition with convolutional neural net…
>There are lots of neural network applications that have little to do with statistics (image recognition with convolutional neural networks for example). You're kidding, right? The most fundamental reasons that deep convnets work at all are statistical in nature.
Well, yes and no. It's like saying you need to understand Bernoulli's priciple before designing the SR-71. True, but 7 levels short of where you're working.