Unsupervised learning is one of the main streams of machine learning, and closely related to multivariate statistics and data mining. This course describes some of the main methods in unsupervised learning, such as principal and independent component analysis, clustering, and nonlinear dimension reduction methods. In recent years, machine learning has become heavily dependent on statistical theory which is why this course is somewhere on the borderline between statistics and computer science. Emphasis is put both on the statistical/probabilistic formulation of the methods as well as on their computational implementation. The course is intended to CS students in the algorithms and machine learning specialisation, to statistics students, and to mathematics students in the statistical machine learning specialisation.