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 I: Basic Statistics or equivalent
Statistics III: Regression analysis or equivalent
Basic knowledge of R