After successful completion of the course, students are able to...

• choose suitable methods for a given problem
• employ suitable technologies, software-tools and standards for the solution of a given problem
• understand principles of machine learning

- linear models for regression and classification (Perceptron, Linear Basis Function Models, RBF, historical overview), applications in computer vision

- neural nets

- error functions and optimization (e.g., pseudo-inverse, gradient descent, newton method)

- model complexity, regularization, model selection, VC dimension 

- kernel methods: duality, sparsity, Support Vector Machine

- principal component analysis and Hebbian rule, canonical correlation analysis

- bayesian view of the above models, bayesian regression

- clustering und vektor quantisierung (e.g., k-means)

- Overview of deep learning models 

• Lecture (will be held online in winterterm 2020W)
• implementation of methods discussed in the lecture using textual instructions
• carrying out experiments using the implemented methods
• documentation including decription and interpretation of results
• oral feedback during assignment interviews

ECTS Breakdown:

4.5 ECTS = 112.5 hours

30     lecture time

70     2 assignments (including studying machine learning principles, 

       reading documents and literature, 
       implementation of learning algorithms in MATLAB or in a similar framework and writing documentation)

2.5    2 interviews (including preparation time)

10     written exam incl. preparation time    

• two assignments
• two assignment interviews (online)
• one written exam (NOTE: due to Covid-19 measures, the exam will be oral and online in the semester 2020W).

Please register for the course in TISS. After registration you can team up as a group of 3 students in TUWEL.

Knowledge of linear algebra and probability theory