Data on the course
Show instruction and examinations
521289S Machine Learning, 5 ECTS cr 
Code 521289S  Validity 01.08.2015 -
Name Machine Learning  Abbreviation Machine Learnin 
Scope5 ECTS cr   
TypeAdvanced Studies Discipline4307 Information Engineering 
TypeCourse   
  Grading1 - 5, pass, fail 
 
   
Unit Computer Science and Engineering DP 

Teachers
Name
Tapio Seppänen 

Description
ECTS Credits 

5 ECTS cr

 
Language of instruction 

English. Examination can be taken in English or Finnish.

 
Timing 

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

 
Learning outcomes 

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 the basics of gradient search method to design a linear discriminant function.

4. can apply regression techniques to practical machine learning problems.

 
Contents 

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

 
Mode of delivery 

Face-to-face teaching, guided laboratory work and independent assignment.

 
Learning activities and teaching methods 

Lectures 10h, Laboratory work 20h, Self-study 20h, Independent task assignment, written examination.

 
Target group 

Students who are interested in data analysis technology. Students of the University of Oulu.

 
Prerequisites and co-requisites 

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

 
Recommended optional programme components 

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

 
Recommended or required reading 

Duda RO, Hart PE, Stork DG, Pattern classification, John Wiley & Sons Inc., 2nd edition, 2001. Handouts.

 
Assessment methods and criteria 

Laboratory work is supervised by assistants who also check that the task assignments are completed properly. The independent task assignment is graded. The course ends with a written exam.

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

 

 
Grading 

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 weighing the written exam by 2/3 and the task assignment by 1/3.

 
Person responsible 

Tapio Seppänen

 
Working life cooperation 

No

 
Other information 

-

 


Current and future instruction
Functions Name Type ECTS cr Teacher Schedule
registration period has not begun Machine Learning  Course  Tapio Seppänen  09.01.18 -06.03.18

Future examinations
Functions Name Type ECTS cr Teacher Schedule
Registration Machine Learning  University exam  Tapio Seppänen 
20.03.18tue 14.30-17.30
Registration Machine Learning  University exam  Tapio Seppänen 
23.04.18mon 14.30-17.30
registration period has not begun Machine Learning  University exam  Tapio Seppänen 
20.09.18thu 14.30-17.30
You may enter WebOodi: