After successful completion of the course, students are able to...
This introductory course teaches the basics of machine learning using computer vision examples. It focuses on a few key areas, on the basis of which the essential laws of machine learning are taught. Students gain a deeper understanding of the methods through independent implementation of the models in the laboratory exercise. The key areas are linear models with fixed basis functions and their kernel extensions as well as optimization methods in supervised and unsupervised learning scenarios. Models with adaptive basis functions and neural networks are introduced. The basic laws that are also dealt with in the exercises are: curse of dimensionality, bias-variance tradeoff and theoretical bounds on the generalization error and the relation to modelcomplexity. We will highlight aspects and challenges of learning from high dimensional data such as images.
In detail, the lecture deals with:
An in-depth understanding of the key methodologies is tought by implementing basic classification and regression methods, enhancing them and evaluating them using image data. Methods are:
ECTS Breakdown:
4.5 ECTS = 112.5 hours
30 lecture time
70 2 assignments (including studying machine learning principles,
reading documents and literature, implementation of learning algorithms and writing documentation)
2.5 2 interviews (including preparation time)
10 written exam incl. preparation time
Please register for the course in TISS. After registration you can team up as a group of 3 students in TUWEL.
Knowledge of linear algebra and probability theory