Introduction to Machine Learning Introduction to Machine Learning Fundamentals of Statistical Methods a. Probability Theory, Bayesian Statistics, Decision Theory Supervised Learning a. Linear Regression, Gaussian…

Machine learning vs. deep learning Logistic regression and multilayer networks Deep learning and optimization (e.g. weight initialization, regularization, data and batch Normalization, dropout, ...) Information…

Linear dynamic systems and probability theory Bayes filter Kalman filter and extened Kalman filter Unscented Kalman filter Motion models Sensor data fusion and association  …

The content covers the fundamentals of the following topics: Fundamentals of Autonomous Driving Environment Perception a. Computer Vision and Machine Learning b. Tracking and Sensor…

Sensors for perception of the traffic situation and navigation Vehicle motion and control Intelligent agents and simulation models (e.g. traffic theory,continuity equation, macro and microscopic…

Measuring systems, analog digital conversion Error of measurements, statistical distribution of measurements Error propagation & compensation, regression analysis Basics of control engineering Steady-state behavior, transient behavior…

Vehicle development processes, methods and tools Requirements engineering Model-based function development Bus systems (e.g. CAN, LIN, MOST, Flexray) Testing of systems and diagnostics