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Models, Algorithms and Data: Introduction to Computing
Last Updated: 2026-02-05 15:41:38
Abstract
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
Objective
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.
Resources
Lecture Notes
https://www.cse-lab.ethz.ch/teaching/mad_fs20Lecture Notes
Literature
1. Introduction to Applied Mathematics, G. Strang 2. Analysis of Numerical Methods, Isaacson and Keller
Learning Materials (Links)
- Main link
- Learning materials
General Information
- Language
- English
- Levels
- BSC
- Frequency
- Yearly recurring
Examination
- Type
- session examination
- Mode
- written 120 minutes
- Aids
- Personal summary, 4 pages (2 sheets) handwritten or machine-typed (single-spaced, font size at least 8 pts.)
Course Components
| Type | Title | Time & Place | Hours |
|---|---|---|---|
| lecture | Models, Algorithms and Data: Introduction to Computing |
|
2 h weekly |
| exercise |
Models, Algorithms and Data: Introduction to Computing
The exercise will start in the 2nd week of the Semester.
|
|
1 h weekly |