Fundamental Computational Methods for data analysis, modeling and simulation relevant to Engineering applications. The course emphasizes the implementation of these methods in Python with application examples drawn from Engineering applications
The course aims to introduce Engineering students to fundamentals of Interpolation, Solution of non-linear equations, Filtering and Numerical Integration as well as the use of novel methods such as Machine Learning and Bayesian Uncertainty Quantification. The course aims to integrate numerical methods with enhancing the students' programming skills.
1. Introduction to Applied Mathematics, G. Strang 2. Analysis of Numerical Methods, Isaacson and Keller
Prerequisites / Notice
A course on the interface of classical (first principle) and Data driven models in computing. Fundamental algorithms for inference, approximation and optimisation. Bridging the gap of Computational and Data sciences.
Performance assessment information (valid until the course unit is held again)