Winter Term

Lecture: MLD - Machine Learning und Data Science

Lecturer: Dr. Jens Trilling

Learning Objectives:

Students will be able to use data generated during the development process of technical products to derive insights for the further development of the product or for subsequent or related products. Students will be familiar with the current capabilities and limitations of data analysis and machine learning methods. They will be able to use existing software to solve specific problems. In addition, they will be able to develop their own software, including by utilizing existing software packages.

Content:

The course begins with a series of lectures introducing the possibilities and limitations of data analysis and machine learning. The limitations of these methods are also explained:

• Preparation of the data used

• Imputing missing data

• Working with uncertain data

• Methods for constructing meta-models

• Components of an artificial neural network

• Supervised learning

• Unsupervised learning (dimension reduction methods, pattern recognition, interpretation, and visualization)