392140 Introduction to Machine Learning (int. Track) (V) (SoSe 2016)

The lecture will introduce basic techniques in machine learning, in particular probability based methods. It starts by (re)-introducing regression in a Bayesian framework as maximum likelihood and maximum a posteriori estimationand proceeds by regarding parameter estimation as a probabilistic process. It introduces concept learning and some of its most popular and widespread applications, e.g. classifaction of data given in form of list of attributes and decision trees. Further topics are the general Expectation-Maximisation, in particular to optimize Gaussian mixture models
and Radial Basis Function networks.

Requirements for participation, required level

Good knowledge of mathematics as taught in the first semesters is indispensible. Basic knowledge of probablities is required. Participation in the previous lecture "Introduction to Neural Networks" is helpful, but not required.

The lecture is part of the international track and will be given in English.

Bibliography

There will be lecture notes available.