Some people like to do crossword puzzles. I like to do machine learning puzzles. Lucky for me, a new contest was just posted yesterday on Kaggle. So naturally, my lazy Saturday was spent getting elbow deep into the data. The training set consists of a series of ‘skies’, each containing ... [Read more...]

If you’ve ever written code that generates a whole whack of files, you may have came across the following problem when processing them. Using a naming convention wherein files are numbered will  gum up any ordering which is based on string sorting (ls, for example). What you end up ... [Read more...]

Dispersal is a key process in many domains, and particularly in ecology. Individuals move in space, and this movement can be modelled as a random process following some kernel. The dispersal kernel is simply a probability distribution describing the distance travelled in a given time frame. Since space is continuous, ... [Read more...]

At last weekend’s Hack Ta Ville event here in Montreal, I joined up with some talented urban planners and web devs to realize Vélobstacles. The idea of the project is to crowd source information on cycling conditions around the city. As with any crowd sourcing project, we were ... [Read more...]

This image comes from the cover of Preliminary Papers of the Second International Workshop on Artificial Intelligence and Statistics (1989). Someone abandoned it in the lobby of my building at school. Whatever for, I’ll never know. I just love the idea of machine learning/AI/Statistics evoking a robot hand ... [Read more...]

As an invasion biologist, the process of spatial spread is at the heart of what I do. When I came across this dataset of Walmart store openings since 1962 I couldn’t help but see it as an invasion front which looks a lot like a biological invasion or (albeit slow) ... [Read more...]

A while ago I wrote this post with some R code to visualize the updating of a beta distribution as the outcome of Bernoulli trials are observed. The code provided a single plot of this process, with all the curves overlayed on top of one another. Then John Myles White (... [Read more...]

If you haven’t yet discovered the competitive machine learning site kaggle.com, please do so now. I’ll wait. Great – so, you checked it out, fell in love and have made it back. I recently downloaded the data for the getting started competition. It consists of 42000 labelled images (28×28) of ... [Read more...]

In his 2004 paper in Trends in Ecology and Evolution, Steven Peck argues: Simulation models can be used to mimic complex systems, but unlike nature, can be manipulated in ways that would be impossible, too costly or unethical to do in natural systems. Simulation can add to theory development and testing, ... [Read more...]

In my morning attempt to read the whole internet before beginning work, I came across a program on the BBC website which allows you to see which Olympic athletes are your body doubles. Or rather, which athletes share your height and weight, and therefore your body mass index. Being a ... [Read more...]

Chaos. Hectic, seemingly unpredictable, complex dynamics. In a word: fun. I usually stick to the warm and fuzzy world of stochasticity and probability distributions, but this post will be (almost) entirely devoid of randomness. While chaotic dynamics are entirely deterministic, their sensitivity to initial conditions can trick the observer into ... [Read more...]

Thursday, June 28th, 2012  19h. [Read more...]

Why settle for just one realisation of this year’s UEFA Euro when you can let the tournament play out 10,000 times in silico? Since I already had some code lying around from my submission to the Kaggle hosted 2010 Take on the Quants challenge, I figured I’d recycle it for ... [Read more...]

Browsing commandlinefu.com today, I came across this little one-liner to display which commands I use most often. Here’s what I got: Yep, seems legit. I navigate and look at files a whole bunch (ls, cd, cat), and I do a butt tonne of editing (vim). I sudo like ... [Read more...]

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The recent Hack/Reduce hackathon in Montreal was a tonne of fun. Our team tackled a data set of consisting of Bixi (Montreal’s bicycle share system) station states at one minute temporal resolution. We used Hadoop and mapreduce to pull out some features of user behaviours. One of the ... [Read more...]

Monday, April 30, 2012  14h-16h. Stewart Biology Rm w6/12 (Montreal) guRu: Denis Haine (Université de Montréal) Topics Reproducible research was first coined by Pr. Jon Claerbout, professor of geophysics at Stanford University, to describe that the results from researches can be replicated by other scientists by making available data, procedures, ... [Read more...]

At this Monday’s Montreal R User Group meeting, Arthur Charpentier gave an interesting talk on the subject of quantile regression. One of the main messages I took away from the workshop was that quantile regression can be used to determine if extreme events are becoming more extreme. The example ... [Read more...]

Stewart Biology Building, McGill University (Rm N4/17) Monday, April 24, 2012  14h-16h Dr. Arthur Charpentier (UQàM) In this workshop we will examine difference concepts related to quantiles, and practical issues based on R codes. This workshop will present quantile regression, and the idea of iterative least square estimation. It will ... [Read more...]

Monday, March 26, 2012  14h-16h, Stewart Biology N4/17 Corey Chivers, Department of Biology McGill University This is a meetup of the Montreal R User Group. Be sure to join the group and RSVP. More information about the workshop here. Topics Why would we want to be Bayesian in the first place?  ... [Read more...]