Statistical Learning
Statistical learning is the statistics side of machine learning, and it has applications many areas, from finance and medicine to handwriting recognition. This course focuses on supervised learning, where a set of training data is used to infer a function that can then be applied to new data.
The course treats basic principles and methods of statistical learning, classification and prediction. As part of this the following concepts are studied; basics of regression and discriminant analysis, model selection and model assessment, regularization through shrinkage and smoothing, tree-based methods such as bagging, random forests and boosting, and support-vector machines for classification and regression.
The course replaces the previous course with the same name and course code MT7038, and so cannot be included in the same degree as MT7038.
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Course structure
The course consists of two modules, theory and hand-in assignments.
Teaching format
Teaching consists of lectures, exercise sessions and supervision in computer rooms.
Assessment
Assessment takes place through a written exam, and written and oral presentation of the hand-in assignments.
Examiner
A list of examiners can be found on
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Schedule
The schedule will be available no later than one month before the start of the course. We do not recommend print-outs as changes can occur. At the start of the course, your department will advise where you can find your schedule during the course. -
Course literature
Note that the course literature can be changed up to two months before the start of the course.
Hastie, Tibshirani & Friedman: The Elements of Statistical Learning: Data Mining, Inference, and Prediction (2nd ed). Springer.
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More information
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