Professor Littman's bird's-eye view of machine learning; an example involving medical diagnosis; a machine-learning program for a video green screen, used in television and film; contrasting it with a traditional program to solve the same problem.

The examples use the Python programming language; Dr. Littman shows how to run programming examples from a web browser, which avoids the need to install the software on a computer; more processing power than is available on a typical home computer.

Whether machine learning beats a rhyming rule, taught in school, for determining whether a word is spelled with an ie or an ei; a decision tree is a convenient tool for approaching this problem; using Python to build a decision tree for predictions.

Learning from images and auditory information; tackling the task using a form of computation called a neural network; the characteristics of this powerful tool; comparing decision-tree and neural-network approaches to recognizing handwritten digits.

Taking a deeper dive into neural networks by working through a simple algorithm implemented in Python; the green-screen problem from the first episode; building a learning algorithm that places the professor against a new backdrop.

Understanding the content of an email to know that it is spam; machine learning does so with the Naive Bayes approach; a simple application of Bayes' theorem to a simplified model of language generation; going backward from effects to their causes.