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:
Correspondence of content
| Course | Corresponds course | Description |
| PLA-43126 Machine Learning Methods, 5 cr | PLA-43121 Machine Learning Methods, 5 cr |