This project translates complex research in statistical mechanics and AI from the Rotskoff Group at Stanford into accessible, hands-on learning experiences for high school students.
Major advancements in computer programming are pushing machine learning practices to the forefront of big tech industry standards.
An invaluable exercise for beginner programming students is to teach them new ways to think. This lesson was written with the intention of introducing students to machine learning using image classification. Complexity has long been a barrier to exposing students to artificial intelligence.
Team Final Project: Code for Good. As CS students develop their programming skills, they also need to develop the awareness that programming can have a vital role in mitigating social concerns and improving life.
Students always want to see the results of their work immediately, and they move on to the next challenge as soon as they solve the problem once.
The CyberLearning team at SRI researches innovation in cyber learning education (http://circlcenter.org) and how to assess the learning gains students make in their computational thinking.
The Stanford Intelligent Systems Lab (SISL) conducts research on decisions under uncertainty and optimization problems with applications to autonomous vehicles.
In this student project, students are given a research experience designed to closely mimic those in academia or industry. Their research task is to use smartphone sensors to develop the core algorithm for an accurate, reliable pedometer app which counts a user’s steps.
The goal of this ETP is to introduce students to computer programming in MATLAB that is heavily used in scientific and engineering research, especially in a university research facility like Stanford.