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LCN Introduction to machine learning for bioengineers (english) Students understand basic concepts and methods of machine learning. They can describe them in mathematical terms and can apply them to data using a high-level programming language (julia/python/R). Learning in neural networks (english) Artificial Neural Networks are inspired by Biological Neural Networks. One big difference is
that optimization in Deep Learning is done with the BackProp Algorithm, whereas in biological neural
networks it is not. We show what biologically plausible learning algorithms can do (and what not). Computational neurosciences: neuronal dynamics (english) In this course we study mathematical models of neurons and neuronal networks in the context of biology and establish links to models of cognition. The focus is on brain dynamics approximated by deterministic or stochastic differential equations.
LCN 2 Understanding statistics and experimental design (english) This course is neither an introduction to the mathematics of statistics nor an introduction to a statistics program such as R. The aim of the course is to understand statistics from its experimental design and to avoid common pitfalls of statistical reasoning. There is space to discuss ongoing work. Learning in neural networks (english) Artificial Neural Networks are inspired by Biological Neural Networks. One big difference is
that optimization in Deep Learning is done with the BackProp Algorithm, whereas in biological neural
networks it is not. We show what biologically plausible learning algorithms can do (and what not). Computational neurosciences: neuronal dynamics (english) In this course we study mathematical models of neurons and neuronal networks in the context of biology and establish links to models of cognition. The focus is on brain dynamics approximated by deterministic or stochastic differential equations.
