PLA-43126 Machine Learning Methods, 5 cr

Additional information

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) tut.fi, for obtaining course IDs. The course is only intended for degree students

Person responsible

Jari Turunen

Learning Outcomes

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

Content

Content Core content Complementary knowledge Specialist knowledge
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 

Instructions for students on how to achieve the learning outcomes

The course is completed by approved assignments

Assessment scale:

Numerical evaluation scale (0-5)

Partial passing:

Completion parts must belong to the same implementation



Correspondence of content

Course Corresponds course  Description 
PLA-43126 Machine Learning Methods, 5 cr PLA-43121 Machine Learning Methods, 5 cr  

Updated by: Baggström Minna, 16.10.2018