The general idea of machine learning is you take a set of data, train a model on it, and use this model to annotate new data. This could be to classify whether an image is of a cat or a dog, or to estimate the effect of changing a parameter in an experiment. In an […]
Interpretability, explainability, or the lack of either, have been popular talking points in the deep learning community. The terms are generally ill-defined and the black-box nature of models, such as deep neural networks, makes it challenging to harness their power in real world applications. In this blog post, I explore notions of interpretability and explainability […]
Michael P. J. Camilleri So my Supervisor recently proposed the following problem to me as a potential way of getting the students to think about Principal Components Analysis. I write below my thoughts. [Edit] Thanks to my supervisor for discussing the problem with me. “The MovieFlix Corporation wants to improve the algorithm it uses to […]
Fellow earthlings! Welcome to our blog on Data Science and all things related! We are a group of PhD students applying machine learning and related methods to a variety of problems in fields as diverse as household electronics, brain strokes and mice. Our journey has been fraught with bugs, chalk stained clothes, and mathematical anomalies […]
