Machine Learning (ELE520)

The course starts with an introduction to the fundamental theory, Bayes decision theory. This statistical based theory let us define optimal decision thresholds to distinguish between data elements, represented by so called feature vectors. These decision thresholds are optimal with respect to minimising the expected error rate. The introductory theory assusmes that the statistical functions describing the data are known. This will not be true in practice where we will have to estimate these functions using parametric and non-parametric methods. Alternatively to estimating the statistical functions directly, we can estimate the coefficients in the polynomials describing the decision borders directly. This is introduced with linear discriminant functions where we seek to find the polynomial coefficients the rate of errro expressed by a criteria function. To do this, we use iterative gradient descent techniques. Further to this neural networks are presented as a method to use when linear discriminant functions falls short. In the techniques presented this far, the class to which each data element belongs is assumed known. In the application of clustering techniques we no longer make this assumption and seek to find natural clusters in the data material. Finally methods for evaluating classifier performance are presented. Another imortant aspect of classification is how to characterise the data material as feature vectors. During the course illustrative examples from ongoing research projects withinin biomedical data analysis are presented.

At the end of this course, the student should be able to recognize problems that can be handled by pattern recognition methods. Furthermore, the student shall be able to use the subject terminology acquired throughout the course to state the problem in a precise manner. To be able to solve the problem, the student must be able to implement a classifier by trainjng it using a representative data material and make sure that it is capable of handling new data. The student should be able to handle different type of classifiers and know the theory for these so that specially designed solutions can be made.

Ingen

Introduction to programming (BID230), Mathematical methods 1 (ÅMA100), Introduction to Probability and Statistics (ÅMA110), Mathematical Methods 2 (ÅMA260)

Exercises

Mandatory that 80% of each type of exercises passed. Mandatory work demands (such as hand in assignments, lab- assignments, projects, etc) must be approved by subject teacher three weeks ahead of examination date.

Fagperson(er)

Course coordinator:

Trygve Christian Eftestøl

Head of Department:

Tom Ryen

6 hrs per week with typically 3 hrs lectures and 2 hrs computer exercises using Matlab and 1 hrs theoretical exercises per week.

Master studies at the Faculty of Science and Technology.

Form and/or discussion

R.O. Duda, P.E. Hart, D.G. Stork: Pattern Classification (2nd. ed.). Chap 1-6,9,10

The course description is retrieved from FS (Felles studentsystem). Version 1