And whatever I do will become forever what I've done.don't rehearsemore quotes

# curves: exciting

In Silico Flurries: Computing a world of snow. Scientific American. 23 December 2017

# art + design

Math geek? If you like the clean geometric design of the type posters, you may enjoy something even more mathematical. Design that transcends repetition: Art of Pi, Phi and e posters.

# Visions of Type

## typography and bird songs

Consider the fact that, if you live in a city, birds are essentially the only wildlife that you meet during your day.

Depending on where you live, you might come several species without even trying. In Vancouver, on my short 10 minute walk to work, I have a good chance to see rock doves, crows, mallars, wigeons, hooded mergansers (if I'm lucky), house sparrows, song sparrows, red-winged black birds, white-crowned sparrows, bushtits, black-capped chickadees, northern flickers, and the mother-of-all-honkers: Canada geese.

Birds and letters are everywhere—art of nature and man.

Letter forms, on the other hand, are the art that is also everywhere. Every typeface is an artistic expression.

Regardless where you live, sadly, you are likely to come across mutants like Comic Sans, Arial and Times New Roman. Hideous creatures from the shallows. Try to find Gotham, Gill Sans, Frutiger, or Garamond.

## learning bird songs

Mnemonics of bird songs help you remember the call and recognize the bird. It's so much easier to think "Quick, three beers!" — the call of the Olive-sided flycatcher — rather than "Chirp, chirp, chirp."

The mnemonic captures the cadence and repetition scheme of the song.

For example, if you listen to the white-throated sparrow you can't help but think that this little guy is trying to tell us something.

## the mnemonics

French Zonotrichia albicollis: Baisse ta jupe, Philomène, Philomène, Philomène. How differently we hear!

White-throated Sparrow (Zonotrichia albicollis)

Potato chip!
American Goldfinch (Spinus tristis)

Here here. Come right here, dear.
Baltimore Oriole (Icterus galbula)

Who cooks for you?
Barred Owl (Strix varia)

Fire fire. Where where? Here here! See it, see it.
Indigo Bunting (Passerina cyanea)

Clear. Wick, wick, wick.
Northern Flicker (Colaptes auratus)

Quick, three beers!
Olive-sided Flycatcher (Contopus cooperi)

Where are you? Here I am.
Red-eyed Vireo (Vireo olivaceus)

Chubby chubby cheeks. Chubby cheeks.
Ruby-crowned kinglet (Regulus calendula)

Here sweetie.

See me, pretty, pretty me.
White-crowned sparrow (Zonotrichia leucophrys)

## the posters

If you love birds and typography, these posters are for you.

The mnemonic for the bird's song is presented on a background that proportionally presents the bird's plumage colors.

If you explore the posters, you just might find the bird too.

Potato chip! — song of the American Goldfinch (Spinus tristis). (BUY ARTWORK)
Here here. Come right here, dear. — song of the Baltimore Oriole (Icterus galbula). (BUY ARTWORK)
Who cooks for you? — song of the Barred Owl (Strix varia). (BUY ARTWORK)
Fire fire. Where where? Here here! See it, see it. — song of the Indigo Bunting (Passerina cyanea). (BUY ARTWORK)
Clear. Wick, wick, wick. — song of the Northern Flicker (Colaptes auratus). (BUY ARTWORK)
Quick, three beers! — song of the Olive-sided Flycatcher (Contopus cooperi). (BUY ARTWORK)
Where are you? Here I am. — song of the Red-eyed Vireo (Vireo olivaceus). (BUY ARTWORK)
Chubby chubby cheeks. Chubby cheeks. — song of the Ruby-crowned kinglet (Regulus calendula). (BUY ARTWORK)
Here sweetie. — song of the Black-capped chickadee (Poecile atricapillus). (BUY ARTWORK)
See me, pretty, pretty me. — song of the White-crowned sparrow (Zonotrichia leucophrys). (BUY ARTWORK)
VIEW ALL

# Statistics vs Machine Learning

Tue 03-04-2018
We conclude our series on Machine Learning with a comparison of two approaches: classical statistical inference and machine learning. The boundary between them is subject to debate, but important generalizations can be made.

Inference creates a mathematical model of the datageneration process to formalize understanding or test a hypothesis about how the system behaves. Prediction aims at forecasting unobserved outcomes or future behavior. Typically we want to do both and know how biological processes work and what will happen next. Inference and ML are complementary in pointing us to biologically meaningful conclusions.

Nature Methods Points of Significance column: Statistics vs machine learning. (read)

Statistics asks us to choose a model that incorporates our knowledge of the system, and ML requires us to choose a predictive algorithm by relying on its empirical capabilities. Justification for an inference model typically rests on whether we feel it adequately captures the essence of the system. The choice of pattern-learning algorithms often depends on measures of past performance in similar scenarios.

Bzdok, D., Krzywinski, M. & Altman, N. (2018) Points of Significance: Statistics vs machine learning. Nature Methods 15:233–234.

Bzdok, D., Krzywinski, M. & Altman, N. (2017) Points of Significance: Machine learning: a primer. Nature Methods 14:1119–1120.

Bzdok, D., Krzywinski, M. & Altman, N. (2017) Points of Significance: Machine learning: supervised methods. Nature Methods 15:5–6.

# Happy 2018 $\pi$ Day—Boonies, burbs and boutiques of $\pi$

Wed 14-03-2018

Celebrate $\pi$ Day (March 14th) and go to brand new places. Together with Jake Lever, this year we shrink the world and play with road maps.

Streets are seamlessly streets from across the world. Finally, a halva shop on the same block!

A great 10 km run loop between Istanbul, Copenhagen, San Francisco and Dublin. Stop off for halva, smørrebrød, espresso and a Guinness on the way. (details)

Intriguing and personal patterns of urban development for each city appear in the Boonies, Burbs and Boutiques series.

In the Boonies, Burbs and Boutiques of $\pi$ we draw progressively denser patches using the digit sequence 159 to inform density. (details)

No color—just lines. Lines from Marrakesh, Prague, Istanbul, Nice and other destinations for the mind and the heart.

Roads from cities rearranged according to the digits of $\pi$. (details)

The art is featured in the Pi City on the Scientific American SA Visual blog.

Check out art from previous years: 2013 $\pi$ Day and 2014 $\pi$ Day, 2015 $\pi$ Day, 2016 $\pi$ Day and 2017 $\pi$ Day.

# Machine learning: supervised methods (SVM & kNN)

Thu 18-01-2018
Supervised learning algorithms extract general principles from observed examples guided by a specific prediction objective.

We examine two very common supervised machine learning methods: linear support vector machines (SVM) and k-nearest neighbors (kNN).

SVM is often less computationally demanding than kNN and is easier to interpret, but it can identify only a limited set of patterns. On the other hand, kNN can find very complex patterns, but its output is more challenging to interpret.

Nature Methods Points of Significance column: Machine learning: supervised methods (SVM & kNN). (read)

We illustrate SVM using a data set in which points fall into two categories, which are separated in SVM by a straight line "margin". SVM can be tuned using a parameter that influences the width and location of the margin, permitting points to fall within the margin or on the wrong side of the margin. We then show how kNN relaxes explicit boundary definitions, such as the straight line in SVM, and how kNN too can be tuned to create more robust classification.

Bzdok, D., Krzywinski, M. & Altman, N. (2018) Points of Significance: Machine learning: a primer. Nature Methods 15:5–6.

Bzdok, D., Krzywinski, M. & Altman, N. (2017) Points of Significance: Machine learning: a primer. Nature Methods 14:1119–1120.

# Human Versus Machine

Tue 16-01-2018
Balancing subjective design with objective optimization.

In a Nature graphics blog article, I present my process behind designing the stark black-and-white Nature 10 cover.

Nature 10, 18 December 2017

# Machine learning: a primer

Thu 18-01-2018
Machine learning extracts patterns from data without explicit instructions.

In this primer, we focus on essential ML principles— a modeling strategy to let the data speak for themselves, to the extent possible.

The benefits of ML arise from its use of a large number of tuning parameters or weights, which control the algorithm’s complexity and are estimated from the data using numerical optimization. Often ML algorithms are motivated by heuristics such as models of interacting neurons or natural evolution—even if the underlying mechanism of the biological system being studied is substantially different. The utility of ML algorithms is typically assessed empirically by how well extracted patterns generalize to new observations.

Nature Methods Points of Significance column: Machine learning: a primer. (read)

We present a data scenario in which we fit to a model with 5 predictors using polynomials and show what to expect from ML when noise and sample size vary. We also demonstrate the consequences of excluding an important predictor or including a spurious one.

Bzdok, D., Krzywinski, M. & Altman, N. (2017) Points of Significance: Machine learning: a primer. Nature Methods 14:1119–1120.