You know how there are lots of blog posts out there about machine learning algorithms “in plain English”? They’re popular because many people want to learn about machine learning, but don’t want to be bombarded with heavy maths the moment they start. I agree, I think machine learning should be taught intuition first. I also recognise that people are busy and don’t want to spend hours reading up on algorithms to understand them.

So forget “machine learning in plain English”. Instead, I present some of the most popular algorithms in haiku form. Consider it “machine learning for the busy”.

## Logistic Regression

Inputs weighted, summed.

Passed through logistic function.

Shall we output 1?

## Random Forest

Oh, decision trees!

Alone you’re not so useful.

How ’bout a forest?

## Support Vector Machines

Binary outcomes.

Maximum separation,

Is what is needed.

## Linear Regression

Weighted X is summed.

Perhaps regularised, too.

Gives me real numbers.

## Neural Networks

Backpropagation:

Works well, is a mystery.

But Geoff Hinton knows.

## K Nearest Neighbours

Nothing to learn here.

Find the nearest data points,

And use the average.

## K-means Clustering

Find similar things

By grouping based on distance

There’s no “right” answer.

## Markov Chains

Mr Markov, your

Use of probabilities

Gave us funny text.

## Naive Bayes

We’re gonna pretend

Features are independent,

Naive as that is.

## Leave a Reply