Machine Intelligence I
Type of class Lecture of Computational Neuroscience
Offered by Computational Neuroscience
Instructor Prof. Dr. Klaus Obermayer
Schedule Lecture: Thu 8:00 - 10:00, Tutorial: Thu 14:00 - 16:00
Contact Margret Franke ()
Target audience Master and PhD students
ECTS points 12 (WiSe+SoSe)
Organized by Computational Neuroscience
Part 1: Artificial neural networks. Connectionist neurons, the multilayer perceptron, radial basis function networks, learning by empirical risk minimization, gradient-based optimization, overfitting and underfitting, regularization techniques, applications to classification and regression problems.
Part 2: Learning theory and support vector machines. Elements of statistical learning theory, learning by structural risk minimization, the C Support Vector Machine, kernels and non-linear decision boundaries, SMO optimization, the P-SVM.
Part 3: Probabilistic methods. Reasoning under uncertainty and Bayesian inference; graphical models, graphs vs. distributions, and belief propagation; generative models; Bayesian inference and neural networks; non-parametric density estimation; parametric density estimation and maximum likelihood methods.
Part 4: Projections methods. Principal Component Analysis and Kernel-PCA; independent component
analysis and blind source separation techniques (Infomax, Fast-ICA, ESD).
Part 5: Stochastic optimization. Simulated annealing, mean-field techniques.
Part 6: Clustering and embedding. K-means clustering, pairwise clustering methods, self-organizing maps for central and pairwise data.
Module Components in the Winter Semester:
Lecture: 2 ECTS
Tutorial: 4 ECTS
URL: http://www.bccn-berlin.de/Graduate+Programs/Courses+and+Modules/