A1 – Advanced Pattern Recognition
|This graduate course is intended for Ph.D. students that use (or are planning to use) pattern recognition of their own research. They are facing a (research) problem that may partly be solved by automatic classifiers, but also requires field-specific knowledge to obtain an acceptable solution. Such statistical pattern recognition techniques will be examined with an emphasis on the generalization capabilities of learning systems. The course is given for Ph.D. students of the Advanced School for Computing and Imaging (ASCI). Postdocs and external Ph.D. students may also attend against a moderate fee. It is assumed that the student has some background in linear algebra and statistics, but not specifically in pattern recognition.
For preparation please click here .
|After succesfully completing this course, the student is able to construct a learning system to solve a given simple pattern recognition problem, using existing software.|
|Daily Schedule [deviations may apply]
09:00 – 10:30 Morning lecture
10:45 – 12:30 Morning Matlab exercises
12:30 – 13:30 Lunch
13:30 – 15:00 Afternoon lecture
15:15 – 17:00 Afternoon Matlab exercises
|The course will be concluded by a small project together with a written report to be handed after the course.|