Data 102: Inference

Data102 Notes #

Here are my notes for the Fall 2022 offering of Data 102, Berkeley’s Inference for Data Science course.

Data 102 explores two major concepts: making decisions under uncertainty and modeling the real world. This is all about making assumptions– here are some definitions:

• Frequentist: $y$ (data) is random, $\theta$ (parameter) is fixed
• Bayesian: $y$ is random, $\theta$ is random
• Parametric: Make assumptions about the relationship between $\theta$ and $y$, then use these assumptions to find the best value of $\theta$ given $y$
• Nonparametric: Don’t make any assumptions, and find any good function $f$ such that $\theta = f(y)$

How to contribute #

See the contributing guide for more details!

For the most part, these notes should be pretty complete in terms of content, but could use some cleaning up (as well as more examples).