Kursuse teave

Course aims: - Introduce machine learning models for embedded systems
- Introduce classification and regression algorithms
- Introduce embedded systems used for machine learning
- Acquire the basics of model optimization for embedded machine learning
- Introduce and use tools for machine learning
- Introduce the creation and use of training data in solutions based on embedded systems.

Brief description of the course: Overview of main algorithms used in machine learning.  Basics of model creation.  Building of training data sets and using them.  Main concepts of model optimization and their use for embedded systems.  No special hardware is developed.  Individual tasks and project assignments (by teams of 2-3 members) are solved during the course.

Learning outcomes in the course: After successfully completing the course, the student:
- Has an overview of various machine learning models suitable for embedded systems
- Can choose and use appropriate machine learning model to solve a specific task
- Can choose appropriate hardware to implement machine learning model
- Can use tools to train and build models
- Can create and use training data correctly
- Can optimize models depending on hardware limitations