One of the hardest topics for software engineers to teach themselves is the mathematical foundations of their work. They may use sets, logical operations and graphs every day, but without some background in set theory, logic, graph theory and so on, there is a limit to how effective they can be with their tools.

This course is designed as a first formal look for practicing software engineers at the discrete math topics typically covered at the early undergrad level. It also briefly visits a handful of topics outside of discrete math that may be of interest to software engineers.

Since early in the 20^{th} century, most math education has become exceedingly rigorous, which may have been important to the development of the field, but unfortunately an impediment to the practical, intuitive understanding sought by non-mathematicians. Our course eschews the proof-centric methodology, instead focusing on problem solving and intuition building.

The practical component of this course predominately involves solving small math and programming problems to consolidate your understanding of the content.

This course makes no assumptions about prior math background.

Bradfield did what I had previously thought impossible: they made math interesting! And practical! In this class I developed a healthy skepticism of my own intuition (those darn pigeonholes). If nothing else, understanding Bayes Theorem may quite literally save my life someday.

Tiger Shen

Software Engineer at Braintree

I took Mathematics for Computing to fill in missing fundamentals, and was impressed to see how much it helped me with my later machine learning study. Before taking the course, I donâ€™t think I would have even had the confidence to tackle the math-heavy aspects of a subject like machine learning.

Richard Artoul

Software Engineer at Uber

This course will next be taught by

Tom Alcorn graduated from MIT with a degree in mathematics and now works as a software engineer at Join, developing a next generation query language for building data. Previously he worked at Flux.io and Everquote. He teaches the *Mathematics for Computing* course at Bradfield and has particular interests in machine learning and motorcycles.