Pattern Recognition and Deep Learning
Students acquire knowledge about different methods and algorithms of pattern recognition and deep artificial neural networks. In exercises, students are able to implement the basic algorithms, will apply pattern recognition principles to technical applications, and learn how to evaluate the performance of classifiers.
In this course the basic topics on statistical pattern recognition and deep neural networks are introduced:
Graduates with a Bachelor’s degree
The online part of the study programme takes place in self-study and in the form of group work. For the self-study part of the programme, video lectures with detailed information about the contents and an elaborated script are offered. The script has been developed especially for extra-occupational learners in regard to the didactic concept of Ulm University. It contains breaks for independent study, multiple and single choice tests, quizzes, exercises, etc.
Your mentor will offer online seminars in periodic intervals. These seminars will help you to handle the exercises and work on the learning topics. An online forum for exchange with the other students will also be available.
Basic knowledge in programming and basic concepts of analysis, linear algebra, and probability.