machine learning for astronomers

I spent a bit of vacation time writing in long-term writing projects. The one I found myself wanting to work on is a long-term project called (for now) “Machine learning for astronomers”, in which I go over the basics, and give contextualized (and unsolicited) advice for using machine-learning methods in astrophysics. One of my principal goals is to criticize many uses that fall into the estimator category, and promote methods that can be built into larger, probabilistic inferences. This deprecates most uses of deep learning, and encourages Gaussian Processes. Interestingly, generative adversarial networks (the new rage) are good in this dichotomy, because they are generators that transform probability densities. But I am starting small, working through the detailed mathematics of five methods which I think are so beautiful and simple, everyone should know them.


  1. Can I have access once its done?

    1. Of course! Everything we do is out in the open. You can even see my current outline on github if you look hard enough.