
Mathematics of Machine Learning (2016) - jwdunne
http://datascience.ibm.com/blog/the-mathematics-of-machine-learning/
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mykeliu
Good God that pie chart.

edit: Should've probably elaborated. Not only is a pie chart usually
ineffective at conveying information, but these percentages are totally
arbitrary. There's not really a specific explanation for why linear algebra is
weighted more than multivariate calculus and algorithms & complexity combined,
not to mention the fact that linear algebra and multivariate calculus in
machine learning overlap to a large degree, and they both feed into algorithms
& complexity as well.

And then on top of that, the 3D and shading seem totally unnecessary, but
that's just me.

~~~
trendia
I'm not sure why you're criticizing that 3D pie chart -- big companies pay
lots of money to consultants from IBM to get a chart that good.

~~~
thinkr42
Surely there's a Chart.ly (or alternatively 314Chart.io) that is in the works
somewhere - disrupting this market using tensor flow deep learning networks to
produce the best pie charts possible.

~~~
jwdunne
It's still early stages. I'd recommend a multivariate test on the best pie
chart colours, following Google's astounding results from their search link
colour test.

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dmix
Someone should create a wiki to collect these types of math books/resource
recommendations.

Basically a definitive list of math for AI/CS with subpages for the various
branches (ML, NLP, haskell-esque typed FP, etc) and focused on self-learning
or even hobbyist entertainment instead of focused on being good for formal
university classes - which is hard to discern from just browsing Amazon
reviews which are mostly full of anecdotes from people's old college days.

I remember when I started down the relearning math rabbit hole last year and
found so many threads on HN via the search feature recommending different math
books in each.

It also doesn't help that there are 100 math books written each year thanks to
the backwards university-fueled incentivize systems to write new ones each
year.

I ended up spending a ton of time hunting down the best ones for each subject.
Which always seems like a great opportunity for optimization if someone takes
a crack at it.

Although once you get past the basics of math I've found a good general rule
is to get one the Dover [1] math book series for the particular subject. These
were written largely in the <1990s but are almost always still relevant and
always my favourites. And notably frequently far more succinct than the
university professor ones.

[1] [https://www.amazon.com/s/field-
keywords=dover+math](https://www.amazon.com/s/field-keywords=dover+math)

~~~
rawnlq
Wiki didn't catch on for whatever reason, but github "Awesome ____" lists did:

[https://github.com/sindresorhus/awesome](https://github.com/sindresorhus/awesome)

You can find one for machine learning:

[https://github.com/josephmisiti/awesome-machine-
learning](https://github.com/josephmisiti/awesome-machine-learning)

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ghufran_syed
Come on folks, that's not particularly charitable. _Of course_ the numbers on
the pie chart are arbitrary, the whole article is clearly presented as the
author's opinion. I still found it more useful than just saying "everything is
important".

~~~
nerdponx
It's not that the numbers are arbitrary, it's that the chart flagrantly
implements the 3 biggest "don't ever do this" items taught in data
visualization courses, books, and articles. In an article aimed at novice data
scientists, no less!

It would be like reading an article about what an aspiring engineer needs to
know about lower-level systems programming, and then coming across a snippet
of bubble sort written in the ugliest Perl you can imagine.

~~~
Myrmornis
What on Earth's wrong with it? It's perfectly clear to normal people. And
please, don't reference Tufte. People who go round criticizing data
visualizations and quoting Tufte are like audiophiles -- no-one else notices
or cares.

~~~
f00_
Why does it use green for both Probability and Algorithms? Why does it use red
for both Linear Algebra and Others? Why is it 3d?

Thin font is hard to read

~~~
Myrmornis
> Why does it use green for both Probability and Algorithms? Why does it use
> red for both Linear Algebra and Others?

I don't know very much about variations in human color perception, but to me
at least, all the colors are clearly distinct (Algorithms&Complexity is a
light blue (cyan perhaps), and Others is a sort of pink/purple (magenta
perhaps).

> Why is it 3d?

Why not? Don't be so serious!

~~~
f00_
i may in fact be color blind

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uptownfunk
I know this is probably irrational but because ibm is associated with garbage
Watson I don't take any of their links seriously anymore.

~~~
Houshalter
Watson isn't garbage, it's just vastly over hyped by their marketing
department. The technologies behind it are pretty impressive though.

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j_s
Ask HN: What maths are critical to pursuing ML/AI?
[https://news.ycombinator.com/item?id=15116379](https://news.ycombinator.com/item?id=15116379)
(135 comments, 4 days ago)

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fonnesbeck
Stopped reading when I saw the 3d pie chart atrocity. I'm sure the rest is
great.

