Applied Machine Learning for Engineers (Lecture + Project)
Format: 24 Lectures (12 weeks) + 24 Tutorials Sessions.
Occurrence: Offered every winter semester
Course Level: Master Students (Typically in their 3rd or 4th semester)
- Students learn to use common libraries and apply ML algorithms to various engineering problems, especially in the aerospace domain.
- Students learn to critically evaluate the results obtained through the application of ML methods.
In this course, Machine Learning algorithms are applied to problems arising in aerospace engineering. Lecture topics include:
- Introduction to Python and ML packages
- Basics of linear algebra, statistics and optimization
- Reinforcement learning
- Model Order Reduction (PCA, t-sne, ...)
- Data preparation
- Regression and classification
- Artificial Neural Networks
Main tools used are Python, scikit-learn, PyTorch and TensorFlow (Keras).