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Tübingen Data Analysis Running lecture

Machine Learning II

In this course, students will learn about important topics and techniques in machine learning, with a particular focus on probabilistic models. The course will cover supervised learning (linear regression algorithms, linear discriminants, logistic regression, nonlinear classification algorithms) and unsupervised learning (principal component analysis including several generalizations, k-means, mixture of Gaussians, Expectation-Maximization)

Lecture and exercises, weekly

Bachelor, Master

basic knowledge of linear algebra and probability theory,
basic familiarity with matlab

Graduate Training Center of Neuroscience, Tübingen

Dijkstra
Summer, 3h per week / 5 ECTS
Visit the course website