From 2020-23 I taught at a highly accelerated math program in Pasadena, CA and was given the opportunity to develop and teach a quantitative computer science track for participating 10th-12th graders who had an interest in CS. I developed the curriculum on the fly while teaching the courses, and eventually consolidated it all into this textbook.

The students had already learned a surprising amount of math for their age using a system that is now commercially available at www.mathacademy.com, where they progressed from prealgebra through AP Calculus BC in only three years (6th-8th grade), and by the end of 9th grade had learned the core topics of linear algebra, multivariable calculus, and differential equations. They often expressed an interest in applying the math they had learned, and given my background in both algorithm development and data science, I became the natural candidate to teach the course. (Incidentally, I also developed the adaptive algorithms for the mathacademy.com system itself.)

The classes began with some basic coding exercises (since most of the incoming 10th graders had little to no coding background) after which we moved on to core data structures & algorithms, regression modeling, machine learning models, and game trees/minimax. The capstone project involved reproducing academic research papers in 90's artificial intelligence – specifically, the papers in the Blondie24 research program, which involved using evolutionary algorithms to train neural networks to play board games.

The program ended last month (I've recently relocated out of state), but I figured the book could be useful to anyone looking to get up to speed quickly in the fundamentals of algorithms and machine learning.