Laboratory of Computer and Information Science

Tik-61.184 Informaatiotekniikan erikoiskurssi IV (4 ov) (L)

What's New
24.9. Instructions for Project work.
21.9. Timetable: topics assigned to people.
15.9. Book order (DL 21.9.)
Timetable:indicate topics that you prefer (DL 17.9.)

NOTICE: The time has changed: Tuesdays 14-16 at B333

Lecturer: Docent Samuel Kaski

Assistant: M.Sc.(Tech) Krista Lagus,, room C311
Semester: Autumn 1999
Credit points: 4 cr
Place: Seminar room B333 in the computer science building
Time: Tuesdays 14-16, starting from September 14
Language: English

Self-Organizing Maps

The Self-Organizing Map (SOM) is a widely used neural network algorithm that is especially suitable for visualizing and interpreting large high-dimensional data sets. Already over 3700 scientific publications have been written on the SOM.

 The SOMs are also known as "Kohonen Maps" after their inventor, Academy Professor Teuvo Kohonen, who will give an opening lecture in the beginning of the course (Sept. 14, 14:15). The SOM is one of the main research topics of the Neural Networks Research Centre and the Laboratory of Computer and Information Science; for more information see

 The seminar covers the state of the art of SOM research and applications. It is based on articles from the top researchers on SOM, collected in the new book "Kohonen Maps" (Editors: E. Oja & S. Kaski, Elsevier, Amsterdam, 1999). More details of the book can be found on the Web page of the course. There will be some books available in the laboratory. Additionally, the book "Self-Organizing Maps" (T. Kohonen, Springer, Berlin, 2nd Ed. 1997) is recommended although not necessary for the seminar. It is by far the most comprehensive treatment of SOMs. 

Purchasing the books:

Requirements for passing the course:

To pass "with distinction", at least 95% of the exercises should be solved, and the presentation as well as the project work should be very good.


There will be approximately two presentations each week. Each presentation should last about 35 minutes, so that 10 minutes is left for questions and discussion.

Your task is to present the paper in such a way that the audience can understand the topic based on your presentation (at least in principle...) Some words of advice that may be useful:


Date Speaker Topic Start page
14.9. Introduction to SOM by prof. Teuvo Kohonen;
practical arrangements about the course.
21.9.  Simona Malaroiu Value maps: Finding value in markets that are expensive 15
21.9.  Atte Saarela A SOM-based sensing approach to robotic manipulation tasks 207
28.9.  Markus Varsta Analyzing and representing multidimensional quantitative and qualitative data:Demographic study of the Rhone valley. The domestic consumption of the Canadian families. 1
28.9.  Razvan Cristescu Active learning in Self-Organizing Maps 57
5.10.  Johan Himberg Self-Organizing Maps on non-Euclidean spaces 97
5.10.  Markus Koskela Tree structured Self-Organizing Maps 121
12.10.  Ville Viitaniemi Growing self-organizing networks --- history, status quo, and perspectives 131
12.10.  Olli-Pekka Rinta-Koski On the optimization of Self-Organizing Maps by genetic algorithms 157
19.10. Henri Hakonen Self organization of a massive text document collection 171
19.10. Mikko Syrjälahti Document classification with Self-Organizing Maps 183
26.10.  Jarmo Ritola Self-Organising Maps in computer aided design of electronic circuits 231
26.10.  Timo Mäkelä Modeling self-organization in the visual cortex 243
2.11.  Juha Raitio Topology preservation in Self-Organizing Maps 279
2.11.  Jussi Ahola LVQ and single trial EEG classification 317
9.11. Vuokko Vuori Chemometric analyses with Self Organising Feature Maps: A worked example of the analysis of cosmetics using Raman spectroscopy 335
9.11. Tuomas Pantsar Self-Organizing Maps for content-based image database retrieval 349
16.11.  Klaus Riederer Indexing audio documents by using latent semantic analysis and SOM 363
16.11.  Antti Saarela Self-Organizing Map in analysis of large-scale industrial systems 375
23.11. Tommi Noponen A spatio-temporal memory based on SOMs with activity diffusion 253
30.11. Feedback seminar for project work; enroll by 16.11. by sending email to course assistant.

Project work

The purpose of the project work is to get hands-on experience with applying the SOM to data analysis, as well as on reporting your work to peers. The deadline for the project work is 31.1.2000.

See instructions for the project work.


Paper copies of exercises will be available in the seminar sessions.   Solved exercises may be returned either during seminar sessions or to the course assistant (in room C311). The deadline for solving the exercises is 31.1.2000.

Other CIS courses
September 21th, 1999
Krista Lagus
Samuel Kaski