The course deals with methods of recognition and classification of images. It will have a strong focus on  methods based on learning from exemplars using multi layer convolutional nets, but will also present alternative learning and classification methods. We will study basic theory for this and algorithms based on real data. We will also give an overview of research in the field. A hand in excercise will deal with different methods for classification and a laboratory will produce an implementation of an algorithm for detecting faces in images.

Prerequisites:

Basic courses in linear algebra and statistics. Knowledge about things such as vector, matrix, eigenvector, linear systems of equations, probability distributions, statistical parameters, expectation value, variance and correlation

Image processing and computer vision DD1422 is a good background but not necessary.