Machine Learning

Machine learning is the study of algorithms that improve through experience.

Core Concepts

Supervised Learning

In supervised learning, we have labeled data $(x_i,y_i)$ and want to learn a function $f$ such that $f(x)\approx y$.

The goal is to minimize the expected loss:

${\min }_f{\mathbb{E}}_{(x,y)}[\ell (f(x),y)]$

See also: Regularization, High Dimensional Statistics

Unsupervised Learning

No labels - we want to find structure in the data.

Connections

• Causal Inference uses ML for effect estimation
• Bioinformatics applies ML to biological data
• Regularization is crucial for high-dimensional problems