Course contents *

• Representation and feature extraction in digital images
• principles of recognition and classification, Bayesian decisions
• discriminant functions, neural networks, support vector machines
• learning, optimization of classifiers
• overview of recognition in biological systems
• examples of recognition: handwritten text, faces, objects.

Intended learning outcomes *

After the course you should be able to:

• identify basic notions, terminology, theories models and methods for classification of data,
• develop and systematically evaluate a number of basic methods for classification of data,
• experimentally evaluate algorithms for classification and recognition of objects in digital gray value images,
• choose appropriate method in order to automatically solve a given classification problem,
• know about theories of how the brain processes visual information for classification,

in order to

• be able to solve general problems of data representation and classification,
• be able to implement, analyze and evaluate simple systems for automatic classification of images,
• obtain a broad base of knowledge in order to be able to acquire information about and read literature in the field.

Course Disposition

No information inserted

Specific prerequisites *

Single course students:

SF1604 Linear Algebra, SF1625 Calculus in one variable, SF1626 Calculus in Several Variables, DD1337 Programming or corresponding courses

Recommended prerequisites

SF1901 Probability Theory and Statistics,  DD2431 Machine Learning

Equipment

No information inserted

Literature

Föreläsningsanteckningar, delas ut vid kursstart.

Grading scale *

A, B, C, D, E, FX, F

Examination *

• INL1 - Assignment, 1.5 credits, Grading scale: P, F
• LAB1 - Laboratory Work, 1.5 credits, Grading scale: P, F
• TEN1 - Examination, 3.0 credits, Grading scale: A, B, C, D, E, FX, F

Based on recommendation from KTH’s coordinator for disabilities, the examiner will decide how to adapt an examination for students with documented disability.

The examiner may apply another examination format when re-examining individual students.

In this course all the regulations of the code of honor at the School of Computer science and Communication apply, see: http://www.kth.se/csc/student/hederskodex/1.17237?l=en_UK.

Other requirements for final grade *

Laboratory assignments (LAB1; 1,5 university credits)
Hand in exercise (INL1; 1,5 university credits)
Examination (TEN1; 3 university credits )

Opportunity to complete the requirements via supplementary examination

No information inserted

Opportunity to raise an approved grade via renewed examination

No information inserted

Examiner

Josephine Sullivan

Course web

Further information about the course can be found on the Course web at the link below. Information on the Course web will later be moved to this site.

Offered by

CSC/Robotics, Perception and Learning

Main field of study *

Computer Science and Engineering

Education cycle *

Second cycle

Add-on studies

Discuss with course leader.

Contact

Josephine Sullivan, tel: 790 6136, e-post: sullivan@kth.se

Ethical approach *

• All members of a group are responsible for the group's work.
• In any assessment, every student shall honestly disclose any help received and sources used.
• In an oral assessment, every student shall be able to present and answer questions about the entire assignment and solution.