- Reserach Profile
- Curriculum Vitae
Category: Lecture+Lesson, 3 SWS
ECTS: 6, Winter Semester
University of Bremen
Lecturer: PD Dr. Stefan Bosse
Sensors, digital sensor data, big data
Basics of sensor data acquisition and processing
Basics of machine learning (metrics and taxonomy)
Algorithms and models
Training, learning, prediction, test
The students should understand the basics of machine learning and its tasks, goals, and applications, and gain insights into algorithms and data models. When deep learning, when and why not!
The difference between the model (model representation and data structures) and the learning and prediction algorithms should be understood.
The students should be able to use, distinguish and evaluate different learning methods on different training data in a simple way using simple laboratory exercises with a WEB-based ML kit and analysis tool (execution in the WEB browser or with node-webkit).
Understanding and application of data preprocessing and importance of quantity and quality of the training data.
An understanding of the problems in handling and using ML procedures is to be acquired using practical examples and exercises. The aim is to acquire the ability to independently select suitable ML methods for a specific problem from measurement and testing technology.