Objectives
The course enables to learn many classical machine learning and pattern recognition methods which can be used to build models and systems based on observed data. After the course students understand the main principles of machine learning and pattern recognition methods and steps needed for applying them in real applications. The students especially learn the core concepts of overfitting and underfitting and are able to find a suitable balance between these extremes in a given problem at hand.
The course enables to learn many classical machine learning and pattern recognition methods which can be used to build models and systems based on observed data. After the course students understand the main principles of machine learning and pattern recognition methods and steps needed for applying them in real applications. The students especially learn the core concepts of overfitting and underfitting and are able to find a suitable balance between these extremes in a given problem at hand.
- Opettaja
Jukka Heikkonen