The world is full of noise and uncertainty. To make sense of it, we collect data and ask questions. Is there a tumor in this x-ray scan? What affects the quality of my manufacturing plant? How old is this planet I see through the telescope? Does this drug actually work? To pose and answer such questions, data scientists must iterate through a cycle: probabilistically model a system, infer hidden patterns from data, and evaluate how well our model describes reality.
By the end of this course, you will learn how to use probabilistic programming to effectively iterate through this cycle. Specifically, you will master
- modeling real-world phenomena using probability models,
- using advanced algorithms to infer hidden patterns from data, and
- evaluating the effectiveness of your analysis.
You will learn to use (and perhaps even contribute to)
Pyro throughout this course.