I'd suggest learning about partial derivatives and in particular, the gradient. With an appropriate book, one evening should be enough. Intuitively the negativ

I'd suggest learning about partial derivatives and in particular, the gradient. With an appropriate book, one evening should be enough. Intuitively the negative of the gradient is the direction to ski fastest downhill, and that's why it is heavily involved in optimization problems. Then there are close connections with convexity -- intuitively the inside of most kitchen cereal bowls is convex. The gradient is crucial in uses of Lagrange multipliers in the non-linear cases; in non-linear optimization with constraints, the gradient is central to the Kuhh-Tucker-Karush necessary conditions for optimality. If are in a deep, long, narrow river valley, want to get to the bottom downstream of the river, and ski downhill using the gradient, then will keep crossing the river over and over, traveling many feet across the river for each foot going downstream. So can approximate the valley with an ellipse and ski in much better direction along the long axis of the ellipse. People figured this out long ago -- it's called conjugate gradients. If the river wanders, then that's still more difficult. In the best fitting in ML, may be in such river valleys, and some notes on ML recognize this and warn about using just the gradient. A lot more with gradient is known and at times useful -- Newton iteration, quasi-Newton, etc.