Opintojakson tiedot

Näytä opetus ja tentit
521289S Koneoppiminen, 5 op 
Tunniste 521289S  Voimassaolo 01.08.2015 -
Nimi Koneoppiminen  Lyhenne Koneoppiminen 
Laajuus5 op   
OpiskelumuotoSyventävät opinnot Oppiaine4307 Tietotekniikka 
LajiOpintojakso   
  Arvostelu1 - 5, hyv, hyl 
 
   
Vastuuyksikkö Tietotekniikan ala 

Opettajat
Nimi
Tapio Seppänen 

Kuvaus
Laajuus 

5 ECTS credits.

 
Opetuskieli 

English.

 
Ajoitus 

The course unit is held in the spring semester, during period III. It is recommended to complete the course at the end of studies.

 
Osaamistavoitteet 

After completing the course, student

  1. can design simple optimal classifiers from the basic theory and assess their performance.
  2. can explain the Bayesian decision theory and apply it to derive minimum error classifiers and minimum cost classifiers.
  3. can apply regression techniques to practical machine learning problems.
 
Sisältö 

Introduction. Bayesian decision theory. Parametric and non-parametric classification. Feature extraction. Classifier design and optimization. Example classifiers. Statistical regression methods.

 
Järjestämistapa 

Face-to-face teaching, guided laboratory work and independent assignment. The laboratory works are done on an online system (Mathworks Grader). Student can do the lab works remotely or in the lab using the same online system.

 
Toteutustavat 

Lectures 16 h, Laboratory work 16 h, and Self-study the rest (Independent task assignment).

 
Kohderyhmä 

Students who are interested in machine learning and pattern recognition theory and methods.

 
Esitietovaatimukset 

The mathematic studies of the candidate degree program of computer science and engineering, or equivalent. Programming skills, especially basics of the Matlab.

 
Yhteydet muihin opintojaksoihin 

The course is an independent entity and does not require additional studies carried out at the same time.

 
Oppimateriaali 

Will be informed when the course starts.

 
Suoritustavat ja arviointikriteerit 

Laboratory work is supervised by assistants who also verify that the task assignments are completed properly. The Matworks Grader online system also verifies the completed tasks. The independent task assignment is graded which establishes the grade for the course.

Read more about  assessment criteria at the University of Oulu webpage.

 
Arviointiasteikko 

The course unit utilizes a numerical grading scale 1-5. In the numerical scale zero stands for a fail. The final grade is established by the independent task assignment.

 
Vastuuhenkilö 

Tapio Seppänen

 
Työelämäyhteistyö 

No

 


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