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Unsupervised Learning

Unsupervised learning is a type of machine learning, where instead of inferring a function from training data that can be used to map new examples, the task is to look for patterns in a data set without pre-existing labels.

The course aims to introduce both basic and modern concepts in statistical learning without training data (unsupervised learning) with applications in statistical data analysis. Key concepts reviewed include similarity metrics, linear and nonlinear dimension reduction methods, centroid, distribution and density based methods for cluster analysis, visualization of high dimensional data, hierarchical methods and various validation methods.

You may also be interested in the course MT7038 Statistical learning, which focuses on supervised learning.

Overlapping courses

The material in this course is also covered in part in the course Machine Learning (DA7063), and they should not be included in the same degree if the degree also contains Statistical Learning (MT7038). DA7063 was given for the last time in the spring 2022.

  • Course structure

    The course consists of two elements, theory and hand-in assignments.

    Teaching format

    Instruction is given in the form of lectures, exercise sessions and supervision.

    Assessment

    The course is assessed through a written exam and hand-in assignments.

    Examiner

    A list of examiners can be found on

    Exam information

  • 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.

    Bishop: Pattern Recognition and Machine Learning. Springer.

    Lee & Verleysen: Nonlinear Dimensionality Reduction. Springer.

    Articles.

    List of course literature Department of Mathematics

  • Course reports

  • More information

    New student
    During your studies

    Course web

    We do not use Athena, you can find our course webpages on kurser.math.su.se.

  • Contact