The aim of this exercise is to deepen the understanding of the theory on pattern recognition taught in the related lecture (186.844). Problems in pattern recognition should be solved single-handed with the help of Jupyter Notebooks in Python.
Feature extraction, basics of probability theory (conditional probabilities, marginal distributions, independence, covariance matrices, etc.), Bayes theorem, simple classifiers (kNN, nearest neighbor, persceptron, etc.)
Four Assigments have to be solved single-handed in Jupyter Notebooks in Python. In addition, a report has to be written explaining the solution and results.
ECTS distribution:
