Credit points: 3.0
50% final exam (mandatory grade>55)
50% HW (10% for each one)
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).