IKT215 Pattern Recognition

Teaching language

The primary language for this course is English.

Recommended prerequisites

A general knowledge of statistics is required, equivalent to MA-223 Statistics.

Course contents

This course will introduce students to the principles of pattern recognition. The course includes a review of the principles of probability and Bayes decision theory, and criteria for classification. We will then consider the theory of maximum likelihood and Bayesian learning for parametric pattern recognition. The course also focuses on non-parametric methods such as classification using nearest neighbour rules and discriminant functions.

Learning outcomes

After completing the course, the student should

• have practical skills in statistical pattern recognition

• have practical skills in syntactic pattern recognition

• be able to solve practical problems using pattern recognition-based tools

• be able to develop pattern recognition algorithms from publicly available machine learning datasets

• be able to do basic data analysis and preprocessing of machine learning data

Teaching methods

The course is organized with a combination of lectures, assignments, paper studies, labs, and report writing. The tasks are done individually or in small groups with group supervision. The work load for the average student is approximately 270 hours.

Evaluation

The person responsible for the course decides, in cooperation with student representative, the form of student evaluation and whether the course is to have a midway or end of course evaluation in accordance with the quality system for education, chapter 4.1.

Offered as Single Standing Module

Yes, if there are places available.

Admission Requirement if given as Single Standing Module

Higher Education Entrance Qualification including mathematics R1 and R2 and physics Fysikk 1, or a pass in the preliminary course examination for engineers (which is also offered at University of Agder).

Assessment methods and criteria

Graded portfolio assessment. Information about the content of the portfolio will be given in Canvas at the start of the semester for each seminar.