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The aim of the course is to familiarize the student with the modern concepts of machine learning at the international research level. In particular:
- The student understands the concepts of Bayesian inference and use it to derive a number of different machine learning methods, such as sparse regression models, multi-layered perceptrons, graphical models and Boltzmann Machines
- The student is familiar with learning algorithms based on the maximum likelihood principle and Bayesian posterior estimation
- The student is familiar with stochastic networks of interacting variables, thermodynamic concepts, and Monte Carlos sampling methods
- The student is familiar with a number of approximate inference methods, such as the variational mean field method, belief propagation
- The student is familiar with optimal control theory, stochastic optimal control theory and path integral control theory
- The student is capable to write computer programs to implement the above methods
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The course provides an advanced introduction to the modern view on machine learning with emphasis on the Bayesian perspective. The course is intended for Master's students in physics as well as master's students in artificial intelligence/computer science with sufficient mathematical background. For artificial intelligence/computer science students it is highly recommended to to take the course Statistical machine learning prior to this course.
For physics and math students, this course is the follow-up of the bachelor course Inleiding Machine Learning
The course provides a good preparation for a Masters' specialisation in Theoretical Neuroscience or Machine Learning.
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