One of the most reliable ways to get a high performance machine learning system is to take a low bias learning algorithm and to train it on a massive training set. But where did you get so much training data from? It turns out that in machine learning there's a fascinating idea called artificial data synthesis. This doesn't apply to every single problem, and often takes some thought and innovation and insight. But if this idea applies to your machine learning problem, it can sometimes be an easy way to get a huge training set to give to your learning algorithm. The idea of artificial data synthesis comprises two main variations: the first is creating data from scratch; the second is we can somehow amplify the existing training set or use a small training set and turn that into a large training set. We'll go over both ideas in this class.