Certain systems, such as a program to recognize faces in a visual image, are trained on sample data to a certain level of performance, and then stop learning. Others learn continuously indefinitely.
Some systems learn reflexively, others in service to a larger task.
We will examine in detail neural networks as a learning method, along with other statistical techniques. We will examine more traditional learning algorithms, based on logical inference techniques. We will examine case-based learning, where new problems and solutions are stored nearly verbatim as sources for future solutions. We will examine systems such as genetic algorithms, that attempt to model natural selection as their learning method. We will look into supervised, un-supervised, and reinforcement learning methods.