Machine Learning and Pattern Recognition I
20112012

IMT4612
 5 ECTS
Prerequisite(s)
BSc level basics in statistics and mathematics, Image analysis and processing course (1st semester)
On the basis of
Expected priorknowledge: 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
The studentsdevelops 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 students 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, kNN classifier, basics of neural networks.
 Data clustering: kmeans clustering, SelfOrganizing map.
 Classification and clustering validity testing: leaveoneout, 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
Form(s) of Assessment (additional text)
 Exam (70%
 Exercises (30%)
 Each part must be individually approved of
Grading Scale
Alphabetical Scale, A(best) – F (fail)
External/internal examiner
One internal and one external examiner
Resit examination
For the exam: Ordinary resit examnination.
Examination support
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.
Replacement course for
IMT4611
Additional information
In case there will be less than 5 students that will apply for the course, it will be at the discretion of Studieprogramansvarlig whether the course will be offered or not an if yes, in which form.