This course will provide you with a practical introduction to pattern recognition and machine learning. The techniques are discussed at a level such that you will be able to apply them in your research. The emphasis is on using the computer as a tool for pattern recognition. Starting from the basis for any pattern recognition application, measurements, the topics discussed will be:
• classification;
• evaluation;
• complexity;
• regression;
• feature selection and extraction;
• clustering.
After you have successfully completed this course, you should: • understand pattern recognition theory to such an extent that he/she is able to read recent literature on the topic in engineering-oriented journals (e.g. IEEE Tr. on PAMI);
• know which statistical methods to apply to which problems, on which assumptions they are based and how these methods interrelate;
• be able to construct a learning system to solve a given simple problem, using existing software.
Prior knowledge:
Basic working knowledge of multivariate statistics and linear algebra is required to follow the course. Next to that, it is expected that you have had the course CSE2510 Machine Learning, or something comparable.
You can get PRTools for Python from https://github.com/DMJTax/prtools