Machine learning is increasingly embraced by science and industry, with impressive progress being made in areas such as computer vision, self-driven cars, and game-playing computers. Expectations are that in years to come these fields of research will continue to go through a phase of rapid progress. In physics and engineering, machine learning methodologies are also gaining importance. Chances are therefore, that during your career you will get exposed to machine learning. In this course you will obtain insight in the strengths (and weaknesses) of various machine learning methodologies. The course focuses on numerical aspects via small-scale examples and hands-on experiences that yield a fundamental understanding of what is ‘happening under the hood’ of machine learning algorithms.