What do mice chasing crickets, particle accelerators, solid sponges for natural gas storage, and toddlers with cameras mounted on their heads have in common? All were the subjects of short talks at yesterday afternoon’s “Informal Symposium” on Machine Learning in the Sciences at the University of Oregon, that Teddy Hay, Gabriel Barello, and I co-organized. … Continue reading Machine Learning Mixer
In Part I, I wrote about how I started exploring the topic of machine learning, and we looked briefly described one of its main aims: automating the task of classifying objects based on their properties. Here, I’ll give an example of this in action, and also describe some general lessons I’ve drawn from this experience. … Continue reading Learning about (machine) learning — part II
Machine learning is everywhere these days, as we train computers to drive cars, play video games, and fold laundry. This intersects my lab’s research as well, which involves lots of computational image analysis (e.g.). Nearly everything my students and I write involves writing or applying particular algorithms to extract information from data. In the past … Continue reading Learning about (machine) learning — Part I
The Eighteenth Elephant 