Machine Learning

Machine Learning is the science and art of extracting meaningful information from data. In supervised machine learning, we use a training set of annotated examples to teach a computer to make valid predictions, or to reliably recognize items. In unsupervised learning, we use computers in an exploratory fashion, to discover interesting patterns in data.

Machine learning and pattern recognition are currently taking center stage in applications ranging from autonomous driving to social network analysis and drug development.

This seminar covers the essential techniques typically taught in an introductory lecture, including

  • statistical learning theory
  • generative and discriminative classifiers
  • ridge regression, lasso
  • logistic regression, generalized linear models
  • kernel methods, support vector machine
  • perceptron, multi-layer perceptron, neural networks
  • dimension reduction
  • cluster analysis


On Tuesday, Oct 18th 2016, I will give general guidelines on "how to give a talk", and will outline the different topics. To choose a topic, you must be present on that occasion.

If you join, you will conduct a literature search on your topic, give a 45 min talk and summarize its contents in a report. You will receive 6 ECTS points and a grade based on: content of your talk (1/3), presentation (1/3) and quality of your report (1/3). This is a "Pflichtseminar" that is eligible towards the specialization in Computational Physics.

The seminar has been heavily oversubscribed in the past semesters. If you wish to participate, please send an email to lab manager Barbara Werner First come, first serve. If you have previously sent me an email, please forward your original message to the above address.


Tuesdays at 11:15, in seminar room 10 of Mathematikon, INF 205, from Oct 18th, 2016.