SML201 AI

Princeton University
Center for Statistics and Machine Learning - SML 201 AI

The Center for Statistics and Machine Learning at Princeton University has openings for AIs to teach precepts for SML 201: Introduction to Data Science during the Fall 2020 semester. The course covers approaches and techniques for obtaining, organizing, exploring, and analyzing data. The material covered also includes predictive modeling, R programming for data science, and the basics of statistical inference (random variables, sampling, probability distributions, parameter estimation, hypothesis testing, and linear regression.)

We teach introductory programming with an approach inspired by functional CS1 courses, and emphasize simulation-based inference in our pedagogy.

AIs are required to lead an 80-minute precept and to attend two course lectures (either Tu/Th 11-12:20 or Tu/Th 3-4:20) each week. AIs will also grade written student work.

Instructor: Daisy Huang
Appointment per precept: 3 hours
Requirements: Background in statistics and programming. Enthusiasm for teaching beginners. Proficiency in R is an asset. We welcome applications from students in both technical disciplines and the candidates working with data in the social sciences.

Interested candidates should contact Daisy Huang with a resume/CV, as soon as possible.  Please copy Susan Johansen on the email.  Applications are accepted until all positions are filled.