Lehrinhalte
Basics in python programming; Exploratory data analysis and visualization; Ordinary machine learning methods; Neural network and deep Learning methods; Gaussian process, Bayesian optimization and adaptive design; Forward prediction models and inverse design models. Applications to materials science problems with hands-on tutorials
Offizielle Kursbeschreibung
After the module the students should have gained an overview on and fundamental understanding of the most relevant machine learning algorithms for experimental characterization, theoretical simulations, and in general statistical analysis in materials science. They will be able to choose and apply appropriate methods to basic materials science problems. The students are able to work with available packages within Python to develop their own simple machine learning based programs, and are going to tackle a challenging project in team work guided by the instructors.
Online-Angebote
moodle
Basics in python programming; Exploratory data analysis and visualization; Ordinary machine learning methods; Neural network and deep Learning methods; Gaussian process, Bayesian optimization and adaptive design; Forward prediction models and inverse design models. Applications to materials science problems with hands-on tutorials
Offizielle Kursbeschreibung
After the module the students should have gained an overview on and fundamental understanding of the most relevant machine learning algorithms for experimental characterization, theoretical simulations, and in general statistical analysis in materials science. They will be able to choose and apply appropriate methods to basic materials science problems. The students are able to work with available packages within Python to develop their own simple machine learning based programs, and are going to tackle a challenging project in team work guided by the instructors.
Online-Angebote
moodle
- Lehrende: Till Frömling
- Lehrende: Binbin Lin
- Lehrende: Baixiang Xu
- Lehrende: Hongbin Zhang
Semester: Verão 2022