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APh/MS 141
Introduction to Computational Methods for Science and Engineering
9 units (3-0-6)  | third term
Prerequisites: graduate standing or instructor's permission.

A broad introduction to scientific computing using the Python language. Introduction to Python and its packages Matplotlib, Numpy and SciPy. Numerical precision and sources of error. Root-finding and optimization. Numerical differentiation and integration. Introduction to numerical methods for linear systems and eigenvalue problems. Numerical methods for ordinary differential equations. Finite-difference methods for partial differential equations. Discrete Fourier transforms. Introduction to data-driven and machine learning methods. Singular value decomposition. Deep learning with PyTorch and Keras. Students will develop numerical calculations in the homework and in a final project. Not offered 2023-24.

Instructor: Bernardi