Contact hours
8 SWS (2 SWS exercises is self-study time; 2 SWS tutorial + 2 SWS lecture + 4 SWS lab course (2 SWS of which is online, time still to be determined))

Credits
10 CP

Workload
300 h

Self study
180 h

Semester
Winter Semester

Cycle
every winter semester

Duration
1 Semester

Group size
40 participants

Recommended prior knowledge
Linear algebra (vectors, matrices, eigenvectors, eigenvalues, ...),
Calculus (functions, derivatives, ...),
Probability theory (joint/marginal/contidional probabilities in multiple variabels, Bayesian theorem, …),
Programming (fluent in at least one programming language, ideally python).

Requirements for participation
None.

Learning Outcome
After the successful completion of this course the students
• know fundamentals of machine learning,
• know a number of important unsupervised learning methods,
• can discuss and decide which of the methods are appropriate for a given data set,
• understand the mathematics of these methods,
• know how to implement and apply these methods in python,
• have gained experience in organizing and working in a team,
• know problem solving strategies like brain storming,
• can communicate about all this in English.

Content
This course first introduces into the field of machine learning and covers some basic concepts, such as learning paradigms, training, testing and generalization, and over/underfitting. It then covers a variety of unsupervised methods from classical machine learning such as principal component analysis, independent component analysis, vector quantization, clustering, Bayesian theory, and graphical models. These are shallow methods, this course does not cover deep learning methods by default, but students may use deep learning in the problems.
On the practical side the students get simple problems to solve in group work and then present to the whole course. In this context, the course will also cover some soft skills, such as concept mapping, team reflection, and brainstorming.

Learning Methods
Lecture + self-studied exercises + inverted classroom style discussion of lecture and exercises + practical problem solving in groups + peer reviewing + presentation of results.

Examination forms
Pass/fail on group work plus graded written final module examination of 90 minutes.

Requirements for the awarding of credit points
Passed group work and passed final module examination.

Recommended literature
Lecture notes and other material are available through the learnscape (click here to get to a clickable version):