Each participant develops a computational solution to a selected pattern-recognition problem, collects empirical training and test data, and presents the implementation and the experimental results
FEO3272 Project in Pattern Classification and Machine Learning 4.0 credits
This course has been discontinued.
Last planned examination: Autumn 2020
Decision to discontinue this course:
No information inserted
Information for research students about course offerings
The project is done individually, during or after the corresponding course EO3270.
Course offerings are missing for current or upcoming semesters.
Headings with content from the Course syllabus FEO3272 (Spring 2014–) are denoted with an asterisk ( )
Content and learning outcomes
Course contents
Intended learning outcomes
After passing this course the student should be able to
-
implement computational solutions to problems in data classification or regression, for example in the form of Matlab code,
-
evaluate and critically analyze the results of the implementation, with focus on the potential consequences of model inaccuracies,
-
present the approach and the empirical results in a scientific paper.
Literature and preparations
Specific prerequisites
PhD students in Electrical Engineering or Computer Science who are following, or have passed, the corresponding theory course EO3270.
Recommended prerequisites
The course is intended for PhD students who are following, or have passed, the corresponding theory course EO3270.
Equipment
Computer with Matlab.
Literature
Bishop, C.M (2006). Pattern recognition and machine learning. Springer.
Examination and completion
If the course is discontinued, students may request to be examined during the following two academic years.
Grading scale
No information inserted
Examination
No information inserted
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.
Examination is based on participation in seminars and final written and oral project reports.
Other requirements for final grade
Active participation in initial and final seminars. Presentation of the implementation and the experimental results as a brief written report and as an oral presentation. The presentation should have sufficient accuracy and quality to serve as a preliminary outline for later inclusion in a journal or conference publication.
Opportunity to complete the requirements via supplementary examination
No information inserted
Opportunity to raise an approved grade via renewed examination
No information inserted
Examiner
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.
Further information
Course room in Canvas
Registered students find further information about the implementation of the course in the course room in Canvas. A link to the course room can be found under the tab Studies in the Personal menu at the start of the course.
Offered by
Main field of study
This course does not belong to any Main field of study.
Education cycle
Third cycle
Add-on studies
No information inserted
Contact
Saikat Chatterjee