Contents
Supervised and unsupervised learning, regression, classification, clustering, dimensionality reduction, maximum likelihood estimation, Bayesian inference, support vector machines, decision trees and random forests, k-means algorithm, Gaussian mixture models, expectation maximization algorithm, principal component analysis and linear discriminant analysis, probabilistic graphical models, deep neural networks, convolutional neural networks, optimization, backpropagation.
Requirements
Bachelor students must have 25 CP from basis modules, otherwise there are no formal requirements. However, it is strongly recommended that at least the basis modules covering analysis, linear algebra, probability theory and statistics, graph theory, and programming with Python have been completed.
- Trainer/in: Matthias Fulde
- Trainer/in: Gemma Roig