Pattern recognition

The course is available as a singular course for students on Ph.D. level.

Registration deadline for PhD students at UiT - The Arctic University of Norway: September 1st

Application deadline for other applicants: June 1st. Application code 9303 in Søknadsweb.

The course covers data analysis techniques such as Bayes classifiers, estimation of probability density functions and related non-parametric classification approaches. Further, linear classifiers using least squares are addressed, in addition to simple processing units (neurons) and their extension to artificial neural networks. Linear and nonlinear (using kernel functions) support vector machine classifiers are discussed, in addition to feature extraction and data transformation using eigenvector-based methods such as Fisher discriminants. Methods for grouping, or clustering, data are treated in detail, including hierarchical clustering and k-means. Exercises and problem solving, in addition to practical pattern recognition for data analysis using programming, are strongly emphasized. Basic programming skills are required.

PhD students or holders of a Norwegian master´s degree of five years or 3+ 2 years (or equivalent) may be admitted. PhD students must upload a document from their university stating that there are registered PhD students. This group of applicants does not have to prove English proficiency and are exempt from semester fee.

Holders of a Master´s degree must upload a Master´s Diploma with Diploma Supplement / English translation of the diploma. Applicants from listed countries must document proficiency in English. To find out if this applies to you see the following list:

Proficiency in English must be documented - list of countries

For more information on accepted English proficiency tests and scores, as well as exemptions from the English proficiency tests, please see the following document:

Proficiency in english - PhD level studies

PhD students at UiT The Arctic University of Norway register for the course through StudentWeb .

External applicants apply for admission through SøknadsWeb.

Application code 9303.

All external applicants have to attach a confirmation of their status as a PhD student from their home institution. Students who hold a Master of Science degree, but are not yet enrolled as a PhD-student have to attach a copy of their master's degree diploma. These students are also required to pay the semester fee.

More information regarding PhD courses at the Faculty of Science and Technology is found here.

Knowledge - The student can

• describe the concepts of classification, clustering and dimensionality reduction in data analysis
• use advanced methods from pattern recognition in research that requires classification, dimensionality reduction and clustering methodology
• compare different algorithms with respect to their usefulness and utility in research and development of new applications and methods
• describe important pattern recognition applications in research and society

Skills - The student can

• formulate a pattern recognition problem in a research context, plan and execute the problem solving using advanced methodology
• train and validate a set of pattern recognition algorithms for a research task to quantify their performance and relative merit
• analyse Bayes classifiers in terms of error probabilities
• design linear classifiers for minimization of squared errors and other criteria
• design and analyse nonlinear classifiers in the form of neural networks
• perform feature extraction and data transformation, e.g. using eigenvectors
• explain different clustering algorithms, and analyse their strengths
• implement in practice all methods discussed in the course for analysis of data

General competence - The student can

• write a report that compares several pattern recognition algorithms applied to a research task
• appreciate the importance of pattern recognition in society
• work with pattern recognition methods for analysis of real data

Recommended prerequisites

FYS-2006 Signal processing, STA-1001 Probability and statistics

If you pass the examination in this course, you will get an reduction in credits (as stated below), if you previously have passed the following courses:

• FYS-3012 Pattern recognition 8 ects

Language of instruction and examination

Schedule

Re-sit examination