Syllabus
- implementation of basic ML methods for classification and regression
- learning to use selected ML libraries
- experimental comparison of performance characteristics of different classification methods
- feature engineering
- ensemble techniques
- implementation of basic techniques of unsupervised ML
Annotation
The course is focused on practical exercises with applying machine learning techniques to real data. Students are expected to be familiar with basic machine learning concepts.