The course covers the basic principles and approaches to independent component analysis, concentrating on fast algorithms for separating a number of source signals from their instantaneous mixtures. Sound separation will not be included. Connections to unsupervised learning in neural networks will be pointed out. Some applications will be covered in detail: extraction of meaningful signals from biomedical measurements, as well as finding hidden factors from text documents.