[debbiedowner]

It would be nice to hear about the optimization method with convergence guarantees etc. Introducing the model is nice, but you need to show quality and easiness of fit. You can maybe do this before since you rely on the idea of learning the parameters somehow to motivate the model.

You can relate to NNs for free since it is a linear layer with sigmoid activation.

You can stress it is linear in that your decision boundary is linear.

I don't like how capitalized letters are not random variables but are observations.

You can give some examples of what conditional PDFs P(H=1 | D ) look like and what you can model. In your case if the ideal temp for coffee is 190F and +/- 10 or more and the coffee is bad then you hope that (temp - 190)^2 is a feature input.

Congrats on the book deal!