Machine Intelligence II - Theoretical Lecture
Type of class Lecture
Offered by Computational Neuroscience
Instructor Prof. Dr. Klaus Obermayer
Schedule Thursdays 10:15 - 11:45
Location Technische Universität Berlin, MA 004
Target audience This module is compulsory for students enrolled in the Master program Computational Neuroscience. Module components are compulsory elective or elective for students of other Master and Diploma programs of Berlin’s universities, who wish to specialize in Machine Learning and Artificial Intelligence, and who fulfill the following prerequisites: analysis, linear algebra, probability calculus and statistics, on a level comparable to mathematics courses for engineers (worth 24 credit points); basic programming skills; good command of the English language.
ECTS points 2
Organized by Computational Neuroscience
Topics covered
- Probabilities and densities
* Density estimation
* Maximum likelihood
* Principal Component Analysis
* Hebbian learning
* Kernel PCA
* Independent Component Analysis
* Stochastic optimization
* K-means clustering
* Pairwise clustering
* Self-Organizing Maps
Link to the course page