744855 VU Advanced numerical mathematics B: Mathematical algorithms 1
winter semester 2021/2022 | Last update: 03.12.2021 | Place course on memo list- Knowledge of the mapping of reality and physics in the computer, best practice for representing objects in the digital world.
- Ability to adapt data structures and algorithms to solve a problem.
- Ability to perform data visualisation: Dimensionality of data and its reduction.
- Knowledge of advanced methods of data visualization, slicing space and time.
- Fitting methods can be applied: Linear, nonlinear, different norms and weightings, total and partial.
- The foundations of Bayesian parameter estimation and of machine learning are known.
We learn to represent reality and physics in the computer and the best ways to deal with objects in the digital world.
We discuss suitable data structures and algorithms to solve physical problems.
Looking at data visualisation as an important field in science: Dimensionality of data and its reduction.
Starting from that come advanced methods of data visualization for example slicing of space and using time (clips).
Fitting methods will be introduced: Linear, nonlinear, different norms and weightings, total and partial.
The foundations of Bayesian parameter estimation and of machine learning are discussed.
- Faculty of Mathematics, Computer Science and Physics
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