Course Overview

​This course covers a variety of shallow unsupervised methods from machine learning such as principal component analysis, independent component analysis, vector quantization, clustering, Bayesian theory and graphical models.

Learning Achievement

Competence

Course prerequisites

Grading Philosophy

The mathematical level of the course is mixed but generally high, including 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, ...). Programming is done in Python, thus the students should have a basic knowledge of that as well, or at least be fluent in another programming language.

Course schedule

Course type

Online Course Requirement

Instructor

Prof. Laurenz Wiskott

Other information

This course is given in a hybrid of conventional lectures, inverted classroom, and problem based learning. The course starts with a two-week introduction into unsupervised methods of machine learning, providing an overview. The students then work in groups of about 4 on realistic problems that can be solved with these methods. In the first week of a problem, they develop hypotheses and strategies for a solution and identify which methods they want to learn. Then the course agrees on a method to focus on theoretically, which will then be done in an inverted classroom format. The students then try to solve the problem and present their results in a short talk with slides recorded as a video. Thus the students will not only learn about machine learning but also soft skills.
Programme: Applied Computer Science

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