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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 credits.

 
Language of instruction 

English.

 
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 regression techniques to practical machine learning problems.
 
Contents 

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

 
Mode of delivery 

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.

 
Learning activities and teaching methods 

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

 
Target group 

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

 
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 

Will be informed when the course starts.

 
Assessment methods and criteria 

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.

 
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 the independent task assignment.

 
Person responsible 

Tapio Seppänen

 
Working life cooperation 

No

 


Current and future instruction
Functions Name Type ECTS cr Teacher Schedule
registration period has not begun Machine Learning  Course  Tapio Seppänen  11.01.21 -04.03.21

Future examinations
Functions Name Type ECTS cr Teacher Schedule
registration period has ended Machine Learning, Online Exam  Exam  Tapio Seppänen 
24.09.20thu 16.15-18.15
registration period has not begun Machine Learning, Online Exam  Exam  Tapio Seppänen 
25.03.21thu 16.15-19.15
registration period has not begun Machine Learning, Online Exam  Exam  Tapio Seppänen 
26.04.21mon 16.15-19.15
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