Trance opera—Spente le Stellebe dramaticmore quotes

# art: fun

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

# Nature Methods: Points of View

Points of View column in Nature Methods. (Points of View)
1 | Krzywinski M 2016 Intuitive design Nat Methods 13:895.
2 | Krzywinski M 2016 Binning high-resolution data Nat Methods 13:463.
3 | Hunnicutt BJ & Krzywinski M 2016 Neural circuit diagrams Nat Methods 13:189.
4 | Hunnicutt BJ & Krzywinski M 2016 Pathways Nat Methods 13:5.
5 | McInerny G & Krzywinski M 2015 Unentangling complex plots Nat Methods 12:591.
6 | Streit M & Gehlenborg N 2015 Temporal Data Nat Methods 12:97.
7 | Lex A & Gehlenborg N 2014 Sets and Intersections Nat Methods 11:778.
8 | Streit M & Gehlenborg N 2014 Bar charts and box plots Nat Methods 11:117.
9 | Krzywinski M & Cairo A 2013 Storytelling Nat Methods 10:687.
10 | Krzywinski M & Savig E 2013 Multidimensional Data Nat Methods 10:595.
11 | Krzywinski M & Wong B 2013 Plotting symbols Nat Methods 10:451.
12 | Krzywinski M 2013 Elements of visual style Nat Methods 10:371.
13 | Krzywinski M 2013 Labels and callouts Nat Methods 10:275.
14 | Krzywinski M 2013 Axes, ticks and grids Nat Methods 10:183.
15 | Wong B 2012 Visualizing biological data Nat Methods 9:1131.
16 | Wong B & Kjaegaard RS 2012 Pencil and paper Nat Methods 9:1037.
17 | Gehlenborg N & Wong B 2012 Power of the plane Nat Methods 9:935.
18 | Gehlenborg N & Wong B 2012 Into the third dimension Nat Methods 9:851.
19 | Gehlenborg N & Wong B 2012 Mapping quantitative data to color Nat Methods 9:769.
20 | Nielsen C & Wong B 2012 Representing genomic structural variation Nat Methods 9:631.
21 | Nielsen C & Wong B 2012 Managing deep data in genome browsers Nat Methods 9:521.
22 | Nielsen C & Wong B 2012 Representing the genome Nat Methods 9:423.
23 | Gehlenborg N & Wong B 2012 Integrating data Nat Methods 9:315.
24 | Gehlenborg N & Wong B 2012 Heat maps Nat Methods 9:213.
25 | Gehlenborg N & Wong B 2012 Networks Nat Methods 9:115.
26 | Shoresh N & Wong B 2012 Data exploration Nat Methods 9:5.
27 | Wong B 2011 The design process Nat Methods 8:987.
28 | Wong B 2011 Salience to relevance Nat Methods 8:889.
29 | Wong B 2011 Layout Nat Methods 8:783.
30 | Wong B 2011 Arrows Nat Methods 8:701.
31 | Wong B 2011 Simplify to clarify Nat Methods 8:611.
32 | Wong B 2011 Avoiding color Nat Methods 8:525.
33 | Wong B 2011 Color blindness Nat Methods 8:441.
34 | Wong B 2011 The overview figure Nat Methods 8:365.
35 | Wong B 2011 Typography Nat Methods 8:277.
36 | Wong B 2011 Points of review (part 2) Nat Methods 8:189.
37 | Wong B 2011 Points of review (part 1) Nat Methods 8:101.
38 | Wong B 2011 Negative space Nat Methods 8:5.
39 | Wong B 2010 Gestalt principles (part 2) Nat Methods 7:941.
40 | Wong B 2010 Gestalt principles (part 1) Nat Methods 7:863.
41 | Wong B 2010 Salience Nat Methods 7:773.
42 | Wong B 2010 Design of data figures Nat Methods 7:665.
43 | Wong B 2010 Color coding Nat Methods 7:573.
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.