PLA-43126 Machine Learning Methods, 5 cr


The course can be completed with two different implementation methods. The course provides traditional lectures and conducts assignments. However, the teaching material is available on Moodle platform, so it is also possible to complete the study period independently of time and place throughout the academic year. If the student intends to complete the course outside of the lecture period, he / she should contact the person in charge, jari.j.turunen (at), for obtaining course IDs. The course is only intended for degree students


Jari Turunen


Toteutuskerta Periodi Vastuuhenkilö Suoritusvaatimukset
PLA-43126 2019-01 2 Jari Turunen
Approved assignments


Kurssin suoritettuaan opiskelijalla on näkemys automaattisesta luokittelusta sekä valmiudet tehdä itsenäisesti datan luokittelija.


Sisältö Ydinsisältö Täydentävä tietämys Erityistietämys
1. Overview and introduction to the basics of classification: features, patterns and classification & clustering (Features and classes can also be studied using students' own data)     
2. Simplify the features by using the principal component analysis     
3. A more detailed presentation of the classification methods   K-means,Self-Organizing Maps (SOM), (Deep) Neural Networks, Maximum Likelihood Estimator (MLE) etc.  Specific uses of different classification methods 
4. Decision-making and Validation of Results     Repair of results in special situations using for example Markov chains 

Ohjeita opiskelijalle osaamisen tasojen saavuttamiseksi

The course is completed by approved assignments


Numerical evaluation scale (0-5)


Completion parts must belong to the same implementation


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PLA-43126 Machine Learning Methods, 5 cr PLA-43121 Machine Learning Methods, 5 cr  

Päivittäjä: Kunnari Jaana, 05.03.2019