Blog Posts by Andrew B. Collier / @datawookie


Day 3: Variables and Data Types

Month of Julia
Most coding involves the assignment and manipulation of variables. Julia is dynamically typed, which means that you don’t need to declare explicitly a variable’s data type. It also means that a single variable name can be associated with different data types at various times. Julia has a sophisticated, yet extremely flexible, system for dealing with data types. covered in great detail by the official documentation. My notes below simply highlight some salient points I uncovered while digging around. Read More →

Day 1: Installation and Orientation

Month of Julia

As a long-term R user I’ve found that there are few tasks (analytical or otherwise) that R cannot immediately handle. Or be made to handle after a bit of hacking! However, I’m always interested in learning new tools. A month or so ago I attended a talk entitled Julia’s Approach to Open Source Machine Learning by John Myles White at ICML in Lille, France. What John told us about Julia was impressive and intriguing. I felt compelled to take a closer look. Like most research tasks, my first stop was the Wikipedia entry, which was suitably informative.

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Shiny Bayesian Updates

Reading Bayesian Computation with R by Jim Albert (Springer, 2009) inspired a fit of enthusiasm. Admittedly, I was on a plane coming back from Amsterdam and looking for distractions. I decided to put together a Shiny app to illustrate successive Bayesian updates. I had not yet seen anything that did this to my satisfaction. I like to think that my results come pretty close.

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Constructing a Word Cloud for ICML 2015

Word clouds have become a bit cliché, but I still think that they have a place in giving a high level overview of the content of a corpus. Here are the steps I took in putting together the word cloud for the International Conference on Machine Learning (2015).

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ICML 2015 (Lille, France): Day 4

Sundry notes from the fourth day of the International Conference for Machine Learning (ICML 2015) in Lille, France. Some of this might not be entirely accurate. Caveat emptor. Celeste: Variational inference for a generative model of astronomical images (Jeffrey Regier, Andrew Miller, Jon McAuliffe, Ryan Adams, Matt Hoffman, Dustin Lang, David Schlegel, Prabhat) Colour modelled as a 4 dimensional vector. The Physics (Planck’s Law) places some constraints on the components of these vectors. Read More →

ICML 2015 (Lille, France): Day 3

Selected scribblings from the third day at the International Conference for Machine Learning (ICML 2015) in Lille, France. I’m going out on a limb with some of this, since the more talks I attend, the more acutely aware I become of my limited knowledge of the cutting edge of Machine Learning. Caveat emptor. Adaptive Belief Propagation (Georgios Papachristoudis, John Fisher) Belief Propagation describes the passage of messages across a network. The focus of this talk was Belief Propagation within a tree. Read More →

ICML 2015 (Lille, France): Day 2

Some notes from the second day at the International Conference for Machine Learning (ICML 2015) in Lille, France. Don’t quote me on any of this because it’s just stuff that I jotted down during the course of the day. Also much of the material discussed in these talks lies outside my field of expertise. Caveat emptor. Two Big Challenges in Machine Learning (Léon Bottou) Machine Learning is an Exact Science. It’s also an Experimental Science. Read More →

ICML 2015 (Lille, France): Day 1 (Tutorials)

Started the day with a run through the early morning streets of Lille. This city seems to wake up late because it was still nice and quiet well after sunrise. Followed by a valiant attempt to sample everything on the buffet breakfast. I’ll know where to focus my attention tomorrow. ICML2015 The first day of the International Conference on Machine Learning (ICML 2015) in Lille consisted of tutorials in two parallel streams. Read More →

Machine Learning with R Cookbook

Cover of 'Machine Learning with R Cookbook'.

“Machine Learning with R Cookbook” by Chiu Yu-Wei is nothing more or less than it purports to be: a collection of 110 recipes for applying Data Analysis and Machine Learning techniques in R. I was asked by the publishers to review this book and found it to be an interesting and informative read. It will not help you understand how Machine Learning works (that’s not the goal!) but it will help you quickly learn how to apply Machine Learning techniques to you own problems.

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Disney: Quality over Quantity

I read an interesting article in the 10 June 2015 edition of The Wall Street Journal. The graphic below concisely illustrates the focused strategy being adopted by Disney: a steady decline in the number of movies released per year accompanied by a steady ascent in operating margin.

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Hosting Shiny on Amazon EC2

I recently finished some work on a Shiny application which incorporated a Random Forest model. The model was stored in a RData file and loaded by server.R during initialisation. This worked fine when tested locally but when I tried to deploy the application on shinyapps.io I ran into a problem: evidently you can only upload server.R and ui.R files. Nothing else.

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