Machine learning and artificial intelligence are everywhere these days: filtering your spam email, detecting when your credit card is stolen, and predicting media content of interest to you. The algorithms can be helpful and quite powerful. It’s tempting to accept unquestioningly the predictions that computers make, because algorithms seem to be objective and the math that powers them appears inaccessible. But don’t be afraid to question machine learning, and don’t shy away from their complexity! In this workshop, we’ll analyze (a) what machine learning is, (b) how to benefit from its usefulness while interrogating potential harms, (c) what makes it a black box – even to those who write the code, and (d) how to do machine learning yourself. By the end of this 3-session series, you will have your own machine learning program that you’ve created, and you will be prepared to teach introductory machine learning concepts to a middle- or high school audience. Beginners are more than welcome. The activities and projects in the course will be applicable to a wide range of interests and skill levels.

Dates: THREE SESSIONS April 18, April 25, May 2 (10:00 – 12:00)

Leaders: Elissa Levy & Greg Benedis-Grab