Introduction to Machine Learning
The course will cover the following topics:
- Principles of machine learning: over- and underfitting, bias-variance
tradeoff, cross-validation
- Tree-based methods: Decision trees and ensemble methods
- Artificial neural networks (ANN)
- Unsupervised learning: PCA and clustering
Extent:
4 hp
Prerequisites
Statistics III: Regression analysis or equivalent
Basic knowledge of R