- 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, relevance vector machine
- clustering und vektor quantisierung (e.g., k-means)
- Overview of deep learning models
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
