review of The Hundred-Page Machine Learning Book

This is a great concise refresher. It covers many of the same concepts as the machine learning course I took earlier this year - decision trees, linear regression, SVM, neural nets, kNN, expectation maximization, ... - and it's sort of like having a set of high-quality, focused notes.

It also explains some details I've ignored in the past and covers many topics I wasn't familiar with:

I'd like to follow up on the concept of Bayesian hyperparameter learning, which was mentioned but not discussed.

This is an intriguing comment that I'd like to understand better:

...not many supervised learning algorithms can boast that they optimize a metric directly. Optimizing a metric is what we really want, but what we do in a typical supervised learning algorithm is we optimize the cost instead of the metric (because metrics are usually not differentiable). Usually, in supervised learning, as soon as we have found a model that optimizes the cost function, we try to tweak hyperparameters to improve the value of the metric. LambdaMART optimizes the metric directly.