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Yeah the statistics and machine learning applications are a bit hard to get away from. But I would still replace the calculus sequence with discrete math, assum
by brianpgordon 8y ago
Yeah the statistics and machine learning applications are a bit hard to get away from. But I would still replace the calculus sequence with discrete math, assuming the number of math credits had to stay the same. At my school (University of Maryland, College Park) most CS majors just took calculus 1-3, statistics, and one basic discrete math course from the computer science department. The size of calculus seems massive (3/5 of the total math load) compared to the benefit. That precious time could be spent digging deep into linear algebra or combinatorics or graph theory etc, and that's useful for all developers who write algorithms, not just in specific applications' niches.
- mlthoughts2018 8y agoWhy do you think they are mutually exclusive? Generating functions, geometric series, difference equations, integrals over discrete probability distributions, elliptic curve cryptography, general linear programming and optimization, eigenvalue analysis in graph theory, stochastic optimization, all these things involve calculus to solve discrete algorithmic questions. In fact I’d argue it makes calculus more important than what is typically understood as “discrete math” because in order to have the mathematical maturity to really understand the meaning of major discrete math results and theorems, it requires understanding calculus first. If you “just” study discrete math mistakenly believing “that’s where the value is” then you’d end up with a shallow understanding of discrete math, lacking ability to extract value from it except in some narrow sense from rote discrete math application-specific coursework.