Other computational courses at Cornell

• Physics 7682 / CIS 6229 - Computational Methods for Nonlinear Systems. Modular computational laboratory course, based on Python, covering everything from complex networks to dynamical systems to NP-complete problems.
• Math 4250 / CS 4210, Numerical Analysis and Differential Equations (Fall, Vladimirsky). Error analysis, approximation, interpolation, numerical integration; solvers for ordinary and partial differential equations. Useful links.
• CS 4220 / Math 4260 - Numerical Analysis: Linear and Nonlinear Problems (Spring, Van Loan), Numerical linear algebra, nonlinear systems and optimization. See also Matlab references.
• CS 5223 - Numerical Analysis: Linear and Nonlinear Problems. Same as 4220?
• ENGRD 3200 - Engineering Computation (Shoemaker). Taylor-series approximations, numerical errors, condition numbers, operation counts, convergence, and stability, probability distributions, hypothesis testing. Roots of functions, simultaneous linear equations, statistics, regression, interpolation, numerical differentiation and integration, and solution of ordinary and partial differential equations, including an introduction to finite difference methods.
• A&EP 4380, Computational Engineering Physics (Kirkland). (See links).

General Web resources

• The Definition of Numerical Analysis (Trefethen)
• Python scientific lecture notes
• Ten surprises from numerical linear algebra

Topic-related resources

• Optimization
  • Python lectures
• ODE and PDE solutions
  • Coursera: Scientific computing (Kutz)

Information and Links

• Course main page
• Course Wiki for collaborative projects
• Numerical Recipes site and Electronic Copy (for institutional subscribers)
• Setting up Python hints (Myers)