Machine Learning

  • Course id: MTAT.03.227
  • Credits: 4AP (6EC)
  • Lectures: Tue 12-14, Liivi 2-315
  • Lecture/Reading Presentation: Wed 16-18, Liivi 2-402
  • Help Session: Thu 10-12, Liivi 2-402 (Alternating weeks: Feb 14, Feb 28, Mar 13, Mar 27, Apr 24, May 8, May 22)
  • Web:
  • Mailing list:
  • Lecturer: Phaedra Agius, PhD
  • Questions:
    • phaedragius at gmail dot com
    • Phaedra.Agius at
    • Jaak.Vilo at

Project Talks on May 27, 28 - Schedule (pdf)

Machine learning is concerned with the development of efficient learning algorithms that perform well on novel data. With the masses of data available in today's world, the implementation of such algorithms together with a rigorous statistical validation of those methods are essential. This course covers the development of such algorithms (basic optimization theory, support vector machines, classification and regression, training and testing data, data preprocessing, probabilistic methods), the validation of them (cross-validation, statistical evaluative methods and ways of comparing algorithms) and real-world applications of these methods (for example, clustering methods in Bioinformatics, web mining, character and voice recognition ...etc).

At the end of the course the student will be familiar with the various subfields of machine learning, will be able to choose objectively those methods suited for particular datasets, and will independently perform a project related to machine learning. There will also be literature assigned for the course for which reading presentations will be prepared by the students. Finally, there will be some homeworks and practical assignments designed to help in the understanding of the material.

  • Course Details

Grading, Exams, HW, Presentations, Project
Refer to attached document for details.(doc)