Ihaka Lectures – videos for your viewing pleasure
We’re three weeks into the month-long Ihaka Lecture Series, and it has been well received – thank you to those who have turned up in person and online.
Our final speaker, Robert Tibshirani, right, is up on Weds April 3 at the University of Auckland (details here). Robert is Professor of Statistics and Biomedical Data Science at Stanford University.
He is best known for proposing the ‘lasso’, a sparse regression estimator, and describing its relationship to the idea of boosting in supervised classification. He will talk about modern sparse supervised learning approaches that extend the lasso.
In the meantime, you might like to check out the films of the last three spea
kers. First up on March 13 was by Bernhard Pfahringer, left, who is Professor of Computer Science at the University of Waikato.
He is a member of the Weka project, New Zealand’s other famous open-source data science contribution, and here talks about the design and development of Weka and more recent projects.
Next was supposed to be JJ Allaire, the founder and CEO of RStudio, and the author of R interfaces to Tensorflow and Keras. However, ill-health prevented him coming, and our very own Professor Thomas Lumley stepped in.
Thomas talked entertainingly about deep learning, in particular how deep convolutional nets are structured and how they can be remarkably effective, but can also fail, as he puts it, “in remarkably alien ways”.

Following was Dr Kristian Lum, Lead Statistician at the Human Rights Data Analysis Group. Her research has concretely demonstrated the potential for machine learning-based predictive policing models to reinforce and, in some cases, amplify historical racial biases in law enforcement.
She talked about algorithmic fairness, and about ways in which policy, rather than data science, influences the development of these models and their choice over non-algorithmic approaches.
Women all know about the toilet queue in the intermission at concerts – same-sized bathrooms for men and women does not equal efficiency. Women who have ever stood and waited in a long line for the loo while the men come and go with speed – and I think I can say that this is about, roughly, give or take, 100% of us – roll our eyes and laugh about this as we wait. But the anecdote reveals an uncomfortable truth, says Caroline Criado Perez in her book Invisible Women: Exposing Data Bias in a World Designed for Men. Design and services that takes the average male or the needs of the average male as the norm – as is the case with car-crash test dummies and stab-proof vests, among other things – are potentially deadly. The Guardian has