Data analysis and statistics 2009-2010 - IMT4611 - 5 ECTS

Prerequisite(s)

BSc level basics in statistics and mathematics, Image analysis and processing course (1st semester)

On the basis of

Expected prior-knowledge: Understanding of basic statistics like probability density function, variance, etc. Basic analysis and matrix algebra. Digital image Processing with
Mathlab (a student should be able to do some basic manipulations of images)

Expected learning outcomes

This course develops understanding of use of statistical analysis for multidimensional data. It also give fundamentals to understand data analysis from raw measurement values
to higher level decision making in color and image context. The course develops basic understanding for difference between analysis with or without a priori data as well as ways to evaluate results. The methods will be learned in practical sessions, where they will be programmed and tested with real data.
The course is practice oriented, where students learn basics of data analysis useful in color, color image and spectral image analysis and processing. In lectures basics of methods are lectures and in practical session, their usage is practices. The aim is not to get deep theoretical understanding and derivation of methods.

On completion of this course the students will be able to:
- Understand principles how multidimensional statistical methods differ from one dimensional methods.
- Program some basic clustering and classification methods and test their validity.
- Program some basic Neural networks methods and test their validity.
- Extract features from raw, measured values of data to be analysed.
- Understand the distribution of information in statistical analysis and meaning in data representation.
- To apply basic statistical and data analysis methods to color and image data.

Topic(s)

Basics of multidimensional statistical analysis.
- Principal component analysis.
- Data classification: Bayesian classifier, k-NN classifier, basics of neural networks.
- Data clustering: k-means clustering, Self-Organizing map.
- Classification and clustering validity testing: leave-one-out, ground truth.

Practical Laboratory Sessions:
- Write spectral color and image data reading and writing routines by Matlab
- Produce PCA component images and reconstruct spectral images from PCA eigenimages
- Realize some classification methods by Matlab
- Realize some clustering methods by Matlab
- Make simple tests of spectral image segmentation, spectral image categorization etc. using learned methods

Teaching Methods

Lectures
Laboratory work
Net Support Learning
Exercises

Form(s) of Assessment

Written exam, 3 hours
Exercises

Exam (75%), exercises (25%)

Alphabetical Scale, A(best) – F (fail)

External/internal examiner

One internal and one external examiner

Re-sit examination

For the exam: Ordinary re-sit examnination.

None

Teaching Materials

Literature and study materials: Handouts of the material covered in the lectures will be distributed.

• R.O.Duda, P.E. Hart, and D.G. Storck: Pattern Classification. 2nd ed., Wiley, 2001
• Sergios Theodoridis, Konstantinos Koutroumbas. “Pattern Recognition”, third edition. Academic Press.