More detailed coverage of machine learning methods and algorithms, presented from statistical and optimization perspective. The course goes beyond Introduction to Machine Learning in both scope and level of detail, introducing techniques such as latent variable models, nonlinear dimensionality reduction, approximative Bayesian inference, and deep learning. Prerequisites: Introduction to machine learning or similar knowledge. The course is also suitable for mathematics and statistics students interested in machine learning.