# Machine Learning and Pattern Recognition 1 2013-2014 - IMT4612 - 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

Knowledge:

• 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 analyzed.
• 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.

Skills:

• The students can use relevant scientific methods in independent research and development in machine learning and pattern recognition.
• The students are capable of carrying out an independent limited research or development project in machine learning and pattern recognition under supervision, following the applicable ethical rules.

General competence:

• The students can work independently and are familiar with terminology of machine learning and pattern recognition.

## 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

The course will be made accessible for both campus and remote students. Every student is free to choose the pedagogic arrangement form that is best fitted for her/his own requirement. The lectures in the course will be given on campus and are open for both categories of students. All the lectures will also be available on Internet through GUC’s learning management system (ClassFronter).

## 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

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.

IMT4611