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 |
In this course, we will develop and implement machine learning algorithms for concrete applications in the field of computer vision. The main purpose of this course is to gain practical experience with the most common machine learning methods and to learn about their benefits and drawbacks when applied to concrete, relevant problems. The main focus will be on supervised learning methods for classification, such as Support Vector Machines, Boosting methods, Gaussian Process Classifiers and tree-based classifiers, as well as deep learning methods (e.g. deep convolutional neural networks) for representation learning. |
| Links |
TUMonline entry
|