Computing for Applied Mathematics

3 Credits.

This course explores foundational ideas that connect applied mathematics with modern computing. Rather than focusing on programming, the course emphasizes the concepts and reasoning behind algorithms and mathematical methods that underlie data analysis, simulation, and scientific computation. Students will be exposed to a variety of topics that bridge mathematics, statistics, and computer science — including but not limited to Monte-Carlo sampling, dynamic programming, tree search and related algorithms, Huffman trees, pattern matching and regular expressions, Markov chain Monte-Carlo, Metropolis–Hastings algorithm, and the Fast Fourier Transform. The course also introduces key data analysis techniques such as linear and nonlinear regression, decision trees, and random forests. Throughout, the emphasis is on understanding how these methods work, why they are important, and how they are used across scientific, engineering, and data-driven disciplines.