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.
You may also be interested in the course MT7039 Unsupervised learning.
Overlapping course
The material in this course is also covered in part in the course Machine Learning (DA7063), which was given for the last time in the spring 2022. They can still be included in the same degree, but not if that degree also contains either of the courses Unsupervised Learning (MT7039) or Statistical Deep Learning (MT7042).
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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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Course reports
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More information
New student
During your studiesCourse web
We do not use Athena, you can find our course webpages on kurser.math.su.se.
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Contact
