A Non-Technical Introduction to Machine Learning

Why should I read this, and what will I learn?

What is machine learning?

What’s a statistical model?

A framework for understanding ML:

Example of structured data. Each row is a person (a unique observation we’ve made), and each column is a different measurement (called a feature or predictor) of that person.

A common regression model: simple linear regression

Structured training data for our simple linear regression model.
A scatterplot of height vs. annual income. Each blue dot is an individual observation.
The linear regression line (red) plotted against the true data (blue)

A common classification model: KNN

A hand-labelled scatterplot. This example is inspired by CS109A, a course offered at Harvard College in the Fall of 2016.

Lastly, a (very) brief discussion about artificial neural nets…

A toy neural net with 9 artificial neurons. Borrowed from https://en.wikipedia.org/wiki/Artificial_neural_network

Key Takeaways:

A parting message:

Data Scientist. Harvard ‘17.