Artificial Intelligence in Automotive Engineering
Course 0000000618 in WS 2022/3
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 |
Thu, 16:15–17:45, Interims II 004 |
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 aspacets will be related to automotive technology topics. 1. Introduction: What is Intelligence? What is artificial Intelligence? Historic overview, overview Machine Learning topics, self driving cars 2. Perception: Machine Vision, Computer-Vision, Image Processing Feature Extraktion, Color detection, Canny Edge Detection, Hough Lines, Stereovision 3. Supervised Learning - Lineare Regression: Random Sampling & Consensus 4. Supervised Learning - Classification: Decision Trres, Support Vector Machines, k-nearest Neighbours. 5. Unsupervised Learning - Clustering: Decision Trees, k-Means 6. Path Finding: Navigation, Graph Theory, Search Algorithms like A* 7. Introduction to Neuronal Networs: Perceptron, Loss Function, Activation Function 8. Neuronal Networks: Backpropagation, Different Layers 9. Convolutional Neuronal Networks: Paramter, Filter, Visualization, Pooling 10. Recurrent Neuronal Networks 11. Reeinforcemente Learning 13. AI-Development: Hyperparameter Tuning, Training on CPU and GPU, Inference |
|---|---|
| Links |
E-Learning course (e. g. Moodle) TUMonline entry |