ENGR 391
Numerical Methods in Engineering
Fall 2026
Topics
- Numerical error and conditioning
- Roots of nonlinear equations
- Linear systems
- Function approximation
- Regression and interpolation
- Introduction to machine learning
- Numerical differentiation
- Numerical integration
- Initial-value problems
- Numerical ordinary and partial differential equations
- Implementation in GNU Octave and MATLAB
Readings
- T. Sauer, Numerical Analysis, Pearson Education.
- R. Bhat and A. Kaushal, Numerical Analysis, Alpha Science Publishers, ISBN 9781783323463.
- Rolf Wuthrich and Carole El Ayoubi, Numerical Methods for Engineering and Data Science, 1st edition, CRC Press/Taylor & Francis, 2025; ISBN 9781032200699 (hardback), 9781032200750 (paperback), 9781003262121 (ebook); DOI 10.1201/9781003262121.
Sources
- Course catalogue ↗2026–2027
- Syllabus or course outline ↗Fall 2026
- Fall 2026 outline. All three named textbooks are explicitly optional. The schedule identifies the introductory machine-learning reading as optional.