# Machine Learning and Other Topics

### Reinforcement Learning at Lyft

A few comments on the Reinforcement Learning work done by my colleagues at Lyft

### When does the Delta Method approximation work?

I explore when the Delta Method approximation works and fails.

### Bias-Variance Trade-Off

The bias-variance trade-off is a rare insight into the challenge of generalization.

### Information Theory and Entropy

Entropy and its related concepts quantify the otherwise abstract concept of information. A tour reveals its relationship to information, binary encodings and uncertainty. Most intuitively, we're left with a simple analogy to 2D areas.

### Generalized Linear Models

A Generalized Linear Model, if viewed without knowledge of their motivation, can be a confusing tool. It's easier to understand if seen as a two knob generalization of linear regression.

### The Fisher Information

The Fisher Information quantifies the information an observation carries for a parameter. The quantification becomes intuitive once we see it measuring a certain geometric quality.

### Bayesian Optimization

When optimizing a slow-to-evaluate and non-differentiable function, one may think random sampling is the only option--a naive approach likely to disappoint. However, Bayesian optimization, a clever exploit of the function assumed smoothness, disconfirms these intuitions.

### The Exponential Family

The exponential family is a generalization of distributions, inclusive of many familiar ones plus a universe of others. The general form brings elegant properties, illuminating all distributions within. In this post, we discuss what it is, how it applies and some of its properties.

### Motivating the Gini Impurity Metric

We reveal the gini impurity metric as the destination of a few natural steps.

### The Trace as a Measure of Complexity

For a class of models, the trace provides a measure of model complexity that's useful for managing the bias variance trade-off.

### A Brief Explanation and Application of Gaussian Processes

A clever and useful technique for inferring distributions over infinite functions using finite observations.