Pattern Recognition and Deep Learning

In this course the basic topics on statistical pattern recognition and deep neural networks are introduced:

• Introduction to statistical and neural pattern recognition
• Linear and nonlinear classifiers
• Kernel methods and learning deep neural network
• Feature extraction, selection and reduction
• Applications and system performance evaluation

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.

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.

After finishing the module successfully you will get a certificate and a supplement, which will list the contents of the module and the competences you have acquired. The supplement confirms the equivalent of 6 credit points (ECTS).