COURSE DESCRIPTION
An introductory course to the theory and practice of machine learning, recognition, and artificial intelligence
SU1/USU is an introductory course on Machine learning. The students learn traditional techniques of feature-based classification (K-NN, SVM, and Bayessian classifiers), iterative and hierarchical clustering (K-means, agglomerative and divisive), and feature selection/extraction (PCA, forward and backward selection, floating search, branch and bound). The course consists of plenary lectures and computer exercises in Python. This course should be followed by DIZO, SU2, and possibly SFTO.

The first Lab is held in person at the same location as the lecture!
We will vote on the distance learning format.