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Statistisk klassificering/
Statistical classification analysis

Course level: Postgraduate level

Number of credits: 8 hp

Examiner: Dietrich von Rosen and Martin Singull

Course literature: "Discriminant Analysis and Statistical Pattern Recognition" by G.J. McLachlan (2004) and "Statistical Regression and Classification - From Linear Models to Machine Learning" by N. Matloff (2017), as well as articles if needed.

Course content and learning objectives: Likelihood-Based Approaches to classification, Classification via Normal models, Linear and quadratic classifiers, Classification using logistic models, Non-parametric classification, Misclassification error.

After completing the course, the student should be able to:

  • explain and formulate the theoretical concepts important for linear and quadratic classification, as well as logistic regression;
  • understand and use non-parametric classification methods;
  • understand the limitations of the different classification methods;
  • calculate, interpret and evaluate probabilities of misclassification;
  • identify the strengths and weaknesses of different statistical classifiers and use them in practice;
  • implement statistical classifiers using statistical software and draw adequate conclusions.

Organisation: Lectures, projects with presentations, and home assignments.

Examination: Home assignments and projects with presentations.

Prerequisites: Elementary multivariate normal distribution theory, statistical regression analysis.

Grading scale: Pass/Failed

Language of instruction: English

Course web page (in Lisam)

Page manager: karin.johansson@liu.se
Last updated: 2022-01-19