Unsupervised Learning

Course contents:  This course introduces basic and modern concepts of statistical learning without training labels - unsupervised statistical learning, with applications in statistical data analysis. Central concepts covered include similarity measures, linear and nonlinear methods of dimensional reduction, combinatorial, distributional and density-based methods of cluster analysis, hierarchical methods and different validation methods.

Teacher: Chun-Biu Li (cbli@math.su.se); 

TA: Nik Tavakolian (nik.tavakolian@math.su.se)

Course literature: A few textbooks are available under "Resources", modern topics will be covered in terms of journal papers.

Course plan: 2026 Edition

Class Schedule

Remarks: The lecture plan below may subject to change (add or skip some topics) during the semester depending on the progress of the class.

Examiner: Chun-Biu Li

Course Exam & Projects: The course consists of the following parts,

Part 1: (Theory 3 hp) There will be an open-book written exam on  Oct 21, 2024. The exam covers some of the recommended exercises given in all classes. Registration for the exam in Ladok is required to take the exam.

Part 2: (Project 4.5 hp) 3 small projects - on materials in all lectures will be given as hand-in written assignments. 1 additional small project - two students work as a group pick up a dataset (see "Project 4" below for a list of databases), then analyze using any unsupervised learning method with validations and present the result orally for the class (~20 mins) on DATE TBD.

Examination Rules at the Department of Mathematics.

Grading Criteria: See below