One simple illustration is K-nearest neighbor algorithm used for classification. It can be applied to digitizing hand-written characters, detecting hidden packages, etc.

The author presents a rapidly convergent algorithm to solve the general portfolio problem of maximizing concave utility functions subject to linear constraints. The algorithm is based on an iterative ...

His project lets you play simple algorithms as audio using AVR microcontrollers. Now the code work for this is very simple, but he hardware implementation is where things get interesting.

For example, it might enable computers to quickly learn to recognize, and make use of, new words in spoken language. Or it could enable a computer to recognize new instances of a particular object.

Indeed, the first application in which reinforcement learning gained notoriety was when AlphaGo, a machine learning algorithm, won against one of the world’s best human players in the game Go.