Beyond Black-Boxes: Teaching Complex Machine Learning Ideas through Hands-on Exercises in NetsBlox
Brian Broll is a Research Scientist at the Institute for Software Integrated Systems at Vanderbilt University. He holds a Ph.D. from Vanderbilt University in Computer Science and a B.Sc. from Buena Vista University, majoring in mathematics education. His research interests include computer science education and model integrated computing.
Existing approaches to teaching machine learning often use existing pre-trained, black-box models. Given the appropriate support, we believe fundamental concepts like optimization and adversarial examples can be accessible in a hands-on way to high school students. In this talk, we present some of our recent work in developing a curriculum to teach machine learning concepts in a hands-on way to high school students. Our approach focuses on teaching ML ideas by enabling students to develop deep intuition about these complex concepts by first making them accessible to novices through interactive tools, pre-programmed games, and carefully designed programming activities. These activities also are designed to have a high ceiling; motivated students can always dig deeper into any part of the activities as they are all entirely implemented in blocks. Activities include decision tree building, gradient descent, adversarial examples, GANs, and more!