During these three-hour workshops students were introduced to the basic concepts of machine learning – supervised learning and unsupervised learning, including neural networks and deep learning – before engaging in interactive coding exercises with practical programming skills, general workflows and optimization best practices followed by question and answer sessions. Participants - primarily comprised of Ph.D and master's students - as well as some undergraduates, staff, faculty, and post doctorates, were given the chance to work with Scikit-Learn, Tensorflow2.2, and Keras machine learning libraries through open-source web applications like Jupyter Notebook and Google Colab with PACE-ICE clusters provided as the computational resource. Outside of the Bluejeans sessions, participants were encouraged to utilize resources provided on Canvas for continued learning or independent study. 

Through the workshops, many participants felt empowered to apply the concepts they learned to tackle machine learning projects in their respective disciplines ranging from Mechanical Engineering to Language and Design. In an anonymous survey distributed post-workshop, participants were incredibly pleased with the breadth of topics covered as well as the pace of the lectures and opportunity for hands on learning. Zhang and PACE look forward to hosting many more rewarding workshops this fall.