A) In most domains, a nonlinear model is far superior to a linear model. At the very least, generally you need to at least apply a transformation (e.g. logistic) to create a nonlinear model from a linear model, because most problem spaces are nonlinear. A nonlinear model doesn't have to be particularly complex though.
B) Machine learning (i.e. automatic tuning of parameters) is far simpler for the user than manual tuning of parameters. It's not a question of whether you "need" machine learning, but whether it will save you work. In fact, the author here is in denial - he actually says he would do machine learning, but doesn't realize that is what it is: "Instead of doing anything fancy, my program generates the coefficients at random to explore the space. If I wanted to generate a good driver for a course, I’d run a few thousand of these and pick the coefficients that complete the course in the shortest time."
A) In most domains, a nonlinear model is far superior to a linear model. At the very least, generally you need to at least apply a transformation (e.g. logistic) to create a nonlinear model from a linear model, because most problem spaces are nonlinear. A nonlinear model doesn't have to be particularly complex though.
B) Machine learning (i.e. automatic tuning of parameters) is far simpler for the user than manual tuning of parameters. It's not a question of whether you "need" machine learning, but whether it will save you work. In fact, the author here is in denial - he actually says he would do machine learning, but doesn't realize that is what it is: "Instead of doing anything fancy, my program generates the coefficients at random to explore the space. If I wanted to generate a good driver for a course, I’d run a few thousand of these and pick the coefficients that complete the course in the shortest time."