Sendhil starts by introducing concepts with the example of face recognition.
The first step is to transform the engineering problem into an empirical problem. Rather than programming what a face looks like, make it into an empirical learning exercise. Is this a face, yes or no?
Better than adjustment for multiple hypothesis testing (Bonferoni, etc.): check whether the outcomes predicts treatment better than chance. If yes, there's an effect. This requires fewer assumptions than what we typically need.
Application type 2: When predictability itself is of economic interest.
Example: 25% of health spending is in last part of people's life. If we could predict who would die despite of this treatment, we would not need to spend this amount and to make people undergo treatment.