Sublime

Machine learning is often perceived as complex, but it boils down to three main types: supervised learning, unscheduled learning, and reinforcement learning.

deep learning, is fabulous at

a new classification problem, where instead of the patient’s symptoms, the input is the experts’ opinions. But you can apply machine learning to this problem in the same way the experts applied it to the original one. We call this metalearning because it’s learning about the learners.

Machine learners call this process dimensionality reduction because it reduces a large number of visible dimensions (the pixels) to a few implicit ones (expression, facial features).

Peter Norvig, director of research at Google, told me at one point that it was the most widely used learner there, and Google uses machine learning in every nook and cranny of what it does. It’s not hard to see why Naïve Bayes would be popular among Googlers. Surprising accuracy aside, it scales great; learning a Naïve Bayes classifier is just a

on metalabeling – i.e., finding the probability of profit of your own simple basic trading strategy, and not to use it to predict the market directly. Why?

Regression: