Aim of the course: To give an introduction to some basic techniques in data mining and knowledge discovery in data bases (KDD) with special emphasis on methods that are relevant for research in science and technology. The basic theoretical background will be given in the course. In the computer assignments and homework the students will learn to use a commercial software package and also experimental (academic) software. Some coding of basic algorithms will take place.

Application areas: Information retrieval (text mining and web search engines), bioinformatics, sensor technology, medical informatics, image processing and searching.

* Introduction to Data mining (Lars Eldén )

* Basic numerical linear algebra (Lars Eldén)

* Text mining and information retrieval (Lars Eldén)

* Data base methods (Patrik Lambrix)

* Bioinformatics (Bengt Persson)

* Support vector machines (Tommy Elfving)

* Clustering 1+2 (Timo Koski):

* Multilinear image analysis for facial recognition (Lars Eldén)

* Systems biology - large-scale reverse engineering by the Lasso (Michael Hörnquist)

Computer Assignments:

* Matrix computations in Matlab, character recognition using SVD (Lars Eldén)

* Text mining and information retrieval (Lars Eldén)

* Statistical (Timo Koski)

* Data base techniques (Patrick Lambrix)

* Bioinformatics (Bengt Persson)

* Text summarization: extraction of key words and key sentences (Lars Eldén)

Possibly also a 2 week project in one of the above topics.

The course literature will be based on a collection of articles from journals, conference proceedings and books, and material available via WW

Data mining and applications in science and technology, 2+2p.

Lars Eldén, Scientific Computing / Numerical Analysis, Department of Mathematics, LiU.

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