Wissenschaftliches Maschinelles Lernen für Ingenieure

What it is about

You learn the basics of machine learning with a focus on supervised learning, kernel methods and neural networks. You understand training procedures such as backpropagation, know different network architectures (RNN, CNN, GAN, Transformer) and fundamental probabilistic methods such as Gaussian Processes and Variational Inference. In the end you can assess which model class fits a concrete problem and build a simple ML workflow.

What you will be able to do

• Understand kernel methods
• Understand the structure and training of neural networks (backpropagation)
• Select suitable network classes for applications
• Develop a machine-learning workflow from building blocks

What the module consists of

• VorlesungDelivery of course content through lectures and demonstrations
• Online-Übungen / ProgrammieraufgabenPractical implementation and preparation for the project

Teaching method

• Vortrag mit FolienExplanation of theoretical concepts
• Code-Demonstrationen und Software-ToolsUnderstanding of practical implementation
• Autograded Computer ExercisesDeepening and preparation for the final project
• Betreuung durch LehrpersonalClarification of questions and deepening of individual topics