Artificial Intelligence in Automotive Engineering
Course 0000001661 in SS 2023
General Data
| Course Type | lecture |
|---|---|
| Semester Weekly Hours | 2 SWS |
| Organisational Unit | Chair of Automotive Technology (Prof. Lienkamp) |
| Lecturers |
Markus Lienkamp Responsible/Coordination: Frank Diermeyer |
| Dates |
Wed, 15:00–16:00, virtuell |
Assignment to Modules
-
MW2378: Künstliche Intelligenz in der Fahrzeugtechnik / Artificial Intelligence in Automotive Engineering
This module is included in the following catalogs:- Catalogue of non-physics elective courses
Further Information
Courses are together with exams the building blocks for modules. Please keep in mind that information on the contents, learning outcomes and, especially examination conditions are given on the module level only – see section "Assignment to Modules" above.
| additional remarks | The lecture covers all relevant aspects in the field of "Artificial Intelligence" with a special focus on "Machine Learning" and "Deep Learning" techniques. In addition, all theoretical aspects will be related to automotive technology topics. 1. Introduction: What is Intelligence? What is Artificial Intelligence? Historical overview, overview Machine Learning topics, self-driving cars 2. Perception: Machine Vision, Computer-Vision, Image Processing Feature Extraction, Color detection, Canny Edge Detection, Hough Lines, Stereovision 3. Supervised Learning - Linear Regression: Random Sampling & Consensus 4. Supervised Learning - Classification: Decision Trees, Support Vector Machines, k-nearest Neighbours. 5. Unsupervised Learning - Clustering: Decision Trees, k-Means 6. Introduction to Neuronal Networks: Perceptron, Loss Function, Activation Function 7. Neuronal Networks: Backpropagation, Different Layers 8. Convolutional Neuronal Networks: Parameter, Filter, Visualization, Pooling 9. Knowledge Graphs and Graph Neural Networks 10. Recurrent Neuronal Networks 11. Reinforcement Learning 12. AI-Development: Hyperparameter Tuning, Training on CPU and GPU, Inference |
|---|---|
| Links |
E-Learning course (e. g. Moodle) TUMonline entry |