Institute of Computer Science
Courses.cs.ut.ee Institute of Computer Science University of Tartu
  1. Courses
  2. 2018/19 spring
  3. Machine Learning II (LTAT.02.004)
ET
Log in
Attention! Courses webpage might experience technical difficulties on 19.05.2026 between 18.00 and 18.30. Please don't submit any homeworks and modify any pages during this time. Sorry for the inconvenience.

Machine Learning II 2018/19 spring

Previous years: 2008 » 2012 » 2013 » 2014

  • Main
  • Lectures
  • Exercise sessions
  • Grading
  • Piazza forum
  • Upload

X. Model-based clustering techniques

Given by Sven Laur

Brief summary: Phylogenetic trees and hierarchical clustering. Independent mutation model. Hard clustering techniques. K-means clustering as a maximum likelihood estimate. Gaussian mixture model as a generalisation of k-means algorithm. Corresponding hard clustering algorithms.

Slides: PDF

Video: UTTV(2016) UTTV (2015) UTTV (2014)

Literature:

  • Bishop: Pattern Recognition and Machine Learning pages 423 - 448

Complementary exercises:

  • Bishop: Pattern Recognition and Machine Learning pages 455 - 459
  • Practical clustering tasks:
    • Discretisation of continuous variables, such as height and weight or gene expression
    • Finding relations between such discretised signals

Free implementations:

  • Mclust package in R
  • Institute of Computer Science
  • Faculty of Science and Technology
  • University of Tartu
In case of technical problems or questions write to:

Contact the course organizers with the organizational and course content questions.
The proprietary copyrights of educational materials belong to the University of Tartu. The use of educational materials is permitted for the purposes and under the conditions provided for in the copyright law for the free use of a work. When using educational materials, the user is obligated to give credit to the author of the educational materials.
The use of educational materials for other purposes is allowed only with the prior written consent of the University of Tartu.
Terms of use for the Courses environment