Learning and Intelligent Systems, 25 cr
Type of the study module
Intermediate Studies
Contact
Alpo Värri, Joni Kämäräinen, Ari Visa
Learning Outcomes
| - | The student can describe the most important pattern recognition methods and choose the most appropriate one for a given task. the student can evaluate the different methods. |
| - | The student can process a set of measurements and inputs in such a way that wanted phenomena can be recognized of them. The student can estimate the limits of the performance of the designed system. |
| - | After having passed the module the student can explain the basic concepts of learing and intelligent systems. |
| - | The student can follow the literature of the field and take an intelligent method found in the literature into use. |
Prerequisites
Basic knowledge of signal processing is helpful. It can be obtained, for example, from courses like SGN-11000 Signaalinkäsittelyn perusteet or SGN-11006 Basic Course in Signal Processing. Intelligent systems are implemented with software. The student is expected to obtain/possess programming skills from other courses. ( Advisable )
Content
Compulsory courses
| Course | Credit points | Class |
| SGN-41006 Signal Interpretation Methods | 4 cr | IV |
| SGN-42006 Machine Learning | 5 cr | IV |
| Total | 9 cr |
Optional Compulsory Courses
| Course | Credit points | Alternativity | Class |
| SGN-13000 Johdatus hahmontunnistukseen ja koneoppimiseen | 5 cr | 1 | III |
| SGN-13006 Introduction to Pattern Recognition and Machine Learning | 5 cr | 1 | III |
1.
Select 1 courses.
The courses are mutually exclusive and only one of them must be chosen.
Complementary Courses
Should be completed to the minimum study module extent of 25 ETCS
Additional information
Learning and intelligent methods refer to the methods which can be used to carry out tasks which require a certain kind of intelligence such as pattern recognition (classification), prediction and the analysis of various signals. Such properties are needed for example in the machines and devices which function independently or in the analysis of 'big data'. Neurocomputing and fuzzy logic are two known examples of learning and intelligent methods. One central use area of learning and intelligent methods is signal processing and its applications. The minor subject of learning and intelligent systems gives the student basic information about learning and intelligent methods and an ability to apply them especially in signal processing applications. The complementary courses can be used to direct the minor module in either more theoretical or more applied direction.
Only intended as a minor