Credit points: 3.0
60% final exam (mandatory grade>55)
40% HW (4% for the first HW and 9% for each one HW(2-5) )
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).