Statistical Inference and Machine Learning

The course gives a deepened knowledge of the theory underlying statistical inference and provides an introduction to basic concepts within machine learning and deep learning. The course treats likelihood-based methods, asymptotic theory, Bayesian statistics, Markov Chain Monte Carlo algorithms, regularised regression, support vector machines, tree-based methods, neural networks, training, validation, tests of predictive models, cross-validation and gradient-based optimization. The subjects are studied both theoretically and practically in practice sessions focusing on both theory and applications in R and assignments with scheduled time for supervision.