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ECVP tutorial on classification images

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The slides for my ECVP tutorial on classification images are available here. Try this alternative version if the equations look funny.

Mineault_1

(image from Mineault et al. 2009)

The slides are in HTML and contain some interactive elements. They’re the result of experimenting with R Markdown, D3 and pandoc. You write the slides in R Markdown, use knitr and pandoc to produce the slides, and add interaction using D3.
I’m not completely happy with the results but it’s a pretty cool set of tools.

Being able to write slides in Pandoc is great. For example, here’s the code for one of the slides:

# What this tutorial is going to be about
* Classification images (AKA reverse correlation) is a technique for probing the visual system with a wide range of stimuli, to see what makes it tick.
* It is essentially like trying to figure out a complex machine by pushing buttons at random.
* The surprising fact is that this can work if you're clever with the analysis.

This gives you a slide with a title and a few bullet points. If you use Markdown-mode in Emacs you can get an outline view by pressing Shift-Tab, and you reorganise things by copy and paste. Including images is relatively painless, if you want tight control over appearance you can use html img elements and a bit of CSS.

<img style="width: 800px;" alt="" src="figure/kontsevich_tyler_1.png" >

Including interactive D3 examples is best done using iframes. The HTML slide stuff is enough of a hack that you really don’t want to have D3 adding another layer of complexity (except for very simple animations, perhaps).

There are some problems and glitches:


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