Credit points: 3.0
The course provides an introduction to the field of machine learning.
Topics include: (i) Data preperation (feature selection, data cleaning). (ii) Supervised learning (linear models, decision trees, support vector machines, neural networks). (iii) Unsupervised learning (clustering, dimensionality reduction). (iv) Learning theory (bias/variance tradeoff; PAC learning).