The Master's Programme in Machine learning and Data mining gives a strong basic understanding of modern computational data analysis and modelling methodologies. It builds on the strong research of two centres of excellence, appointed by the Academy of Finland, in machine learning and data mining. These methods are applicable and needed in a wide variety of fields ranging from process industry to mobile communications. Recent spearhead application areas include bioinformatics, computational linguistics and natural language processing, and multimodal interfaces.

The Macadamia Master's Programme consists of two years of full-time studies (120 ECTS credits). The studies include

  • basic techniques and theory of machine learning and data mining
  • courses on application areas connected to our research, such as computer vision, bioinformatics, neuroinformatics, and language technology
  • special courses on timely research topics
  • practical exercise projects
  • elective studies

The programme provides an excellent basis for doctoral studies as well as industrial research and development work. In the Finnish education system, admission to doctoral studies requires a Master's degree. The offered courses are a focused part of the major subject Computer and Information Science, one of the majors included in the Degree programme of Computer Science and Engineering at Aalto University School of Science.

Teaching and supervision for Macadamia students is given by an enthusiastic and experienced group headed by world leaders in this research field. They belong to two national Centres of Excellence, the Centre of Excellence in Computational Inference Research (COIN) and the Algorithmic Data Analysis Research Centre (Algodan). The host laboratory is a partner in several Finnish graduate schools.

The professors responsible for Macadamia are:

Erkki Oja

Samuel Kaski

Harri Lähdesmäki

Juho Rousu

Aristides Gionis

Tapani Raiko

Juha Karhunen

Heikki Mannila

 For additional information, see a 6-page publication (Teaching Machine Learning workshop, 2008).