Files for "Introduction to Machine Learning for Social Scientists" (POLISCI251A) given at Stanford University during the summer of 2018.
This course introduces techniques to collect, analyze, and utilize large collections of data for social science inferences. The ultimate goal of the course is to familiarize students to modern machine learning techniques and provide the skills necessary to apply these methods widely. Students will leave the course equipped with a broad understanding of machine learning and on how to continue building new skills. This is an introductory course, so most the lectures and problem sets will be focused on the intuition and the mechanics behind machine learning concepts rather than the mathematical fundamentals. There are no formal prerequisites for the course, but calculus and introductory statistics are strongly recommended. Students are not expected to have any programming knowledge, and the course will be centered around bite-size assignments that will help build R coding and statistical skills from scratch.
Credits: The original version of the course was created by Justin Grimmer. Rochelle Terman developed a second version. Many thanks to both of them.
Materials under development and subject to change