Lecture:
    Takes place every week on Tuesday from 12:15 to 13:45 in room GBCF 04/711.
    First appointment is on 08.10.2019
    Last appointment is on 28.01.2020

Exercise:
    Takes place every week on Tuesday from 09:00 to 12:00 in room GBCF 04/711.
    First appointment is on 15.10.2019
    Last appointment is on 28.01.2020

Language: This course will be given in English.

Goals: (i) The students should get to know a number of unsupervised learning methods. (ii) They should be able to discuss which of the methods might be appropriate for a given data set. (iii) They should understand the mathematics of these methods.

Content: This course covers a variety of unsupervised methods from machine learning such as principal component analysis, independent component analysis, vector quantization, clustering, Bayesian theory and graphical models. We will also briefly discuss reinforcement learning.

Format: This course is given with the flipped/inverted classroom concept. The students work through online material, this will then be deepened in the tutorial with some exercises and then deepened further in the lecture with some general discussion. The exercises are solved in the tutorial in a group effort, not at home. The students therefore have the opportunity to meet already at 9:00 to work on the exercises on their own.

Requirements: The mathematical level of the course is mixed but generally high. The tutorial is almost entirely mathematical. Mathematics required include calculus (functions, derivatives, integrals, differential equations, ...), linear algebra (vectors, matrices, inner product, orthogonal vectors, basis systems, ...), and a bit of probability theory (probabilities, probability densities, Bayes' theorem, ...).

Exam: The course will be concluded with an oral exam. The dates will be set at the end of the semester.

Course Material: Please download all material you need until end of July, because beginning in August I prepare the new course and some links of the old course might break.