|At the end of this course, the student
- is able to formulate a probabilistic model for regression, classification, and density estimation
- understands the principle of maximum likelihood estimation as well as the full Bayesian approach
- is able to derive algorithms using these principles for a wide class of models, such as the linear Gaussian model, linear models for regression, logistic regression, and Gaussian mixtures (the EM algorithm)
- is able to understand and implement mathematically described methods from modern statistical machine learning
|Machine learning is concerned with methods for decision taking based on data. In statistical machine learning, these methods are based on probabilistic models and statistical inference methods, including the maximum likelihood estimate and Bayesian learning. These methods have a wide variety of applications such as visual object recognition, analysis of genetic data, financial data or neuroscience data etc.
In this course we provide a principled treatment of the basic models and methods from statistical machine learning. This requires a certain mathematical depth, but we will take ample time to acquire the necessary mathematical knowledge and skills using exercises, (computer) assignments, and optionally student projects on more advanced state-of-the-art machine learning methods (such as Gaussian processes, support vector machines, graphical models, Markov Chain Monte Carlo.)