Description Would you like to be able to develop and prepare the data you need to pose, explore, and answer the most pressing and complex questions in your field of research? This course concerns itself with one of the most demanding and least covered parts of developing a predictive model for precision agriculture, or just about anything: sampling. When studying machine learning through video tutorials you normally access somebody's dataset and learn how to apply algorithms. But how were those neat datasets created? This course details how to use and adapt to your unique needs some tools I developed to sample just about any spatially explicit variable through the Google Earth Engine Platform.

In science, we use statistics and experiments to figure out causal relationships. Researchers in artificial intelligence and machine learning have started to design software that allows computers to learn about causes the way that scientists do. Over the past 15 years, researchers in my laboratory have shown that children learn in much the same way. In one experiment, we showed preschool children a simple machine with a switch on one side and two disks that spin on top. Then we showed them what would happen if you performed some experiments on the machine; for example, you take off one disk, flip the switch and see what happens to the other disk.