It's interesting how comprehensively this 'AI4All' course traces the AI journey from early perceptrons to current CNNs. How you approach the varying technical backgrounds of students when tackling topics like backpropagation or activaton functions?
Honestly, backpropagation was easier for students to grasp than activation functions. I started from a simple foundation, models learning by using feedback from their mistakes. I built on that with iterative improvement intuition, a bit of friendly anthropomorphism, and the one-time drumroll for “you shall hear this only once! the gradient,” plus the joke about "see, we told you, calculus would finally paying off." From there we advanced, but an intro AI course does not ask students to hand-carry gradients through layers. Activations were tougher: you begin with the step function, then emphasize that activation is a design choice with many options and consequences for signal backpropagation; analogies helped a lot, but the real insight is connecting that choice to training, how the activation’s derivative controls how much signal actually flows backward. I think this went well. If I had to revisit this material, which I will do in Spring, when I am teaching this course again (due to popular demand here at Mason), I would add some more detail to an animation to actually show derivatives as part of the backpropagation algorithm. I removed some of that detail to unclutter the conceptual understanding.
It's interesting how comprehensively this 'AI4All' course traces the AI journey from early perceptrons to current CNNs. How you approach the varying technical backgrounds of students when tackling topics like backpropagation or activaton functions?
Honestly, backpropagation was easier for students to grasp than activation functions. I started from a simple foundation, models learning by using feedback from their mistakes. I built on that with iterative improvement intuition, a bit of friendly anthropomorphism, and the one-time drumroll for “you shall hear this only once! the gradient,” plus the joke about "see, we told you, calculus would finally paying off." From there we advanced, but an intro AI course does not ask students to hand-carry gradients through layers. Activations were tougher: you begin with the step function, then emphasize that activation is a design choice with many options and consequences for signal backpropagation; analogies helped a lot, but the real insight is connecting that choice to training, how the activation’s derivative controls how much signal actually flows backward. I think this went well. If I had to revisit this material, which I will do in Spring, when I am teaching this course again (due to popular demand here at Mason), I would add some more detail to an animation to actually show derivatives as part of the backpropagation algorithm. I removed some of that detail to unclutter the conceptual understanding.