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Berlin Data Analysis Running lecture

Machine Intelligence

Artificial neural networks: Connectionist neurons, the multilayer perceptron, radial basis function networks, learning by empirical risk minimization, gradient-based optimization, overfitting and underfitting. Learning theories and support vector machines: statistical learning, learning by structural risk minimization. Probabilistic methods: Bayesian inference and neural networks, generative models. Projections methods. Principal Component Analysis, Independent Component Analysis and blind source separation. Stochastic optimization. Clustering and embedding.

Lecture Maths Tutorial, weekly

MSc, PhD

Maths: 3 semesters
Programming: basic
Neuroscience: none
Programming language: Python, Matlab, R

Technische Universität Berlin, BCCN Berlin, Berlin

Winter and summer, 2 x 4h per week / 2 x 6 ECTS

Runs for 2 semesters, each semester can be taken separately for 6 ECTS

Visit the course website