| Code | DSP713 |
| Name | Machine learning |
| Status | Compulsory/Courses of Limited Choice |
| Level and type | Post-graduate Studies, Academic |
| Field of study | Computer Science |
| Faculty | |
| Academic staff | Agris Ņikitenko |
| Credit points | 3.0 (4.5 ECTS) |
| Parts | 1 |
| Annotation |
The course addresses the question how to enable computers to learn from past experiences. It introduces the field of machine learning describing a variety of learning paradigms, algorithms, theoretical results and applications.. |
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Goals and objectives of the course in terms of competences and skills |
The objective is to give students fundamental knowledge about the key algorithms and theory that form the foundation of machine learning as well as to train practical skill in machine learning algorithms and methods |
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Learning outcomes and assessment |
Is able to describe the main principles, advantages and limitations of machine learning - Appropriate questions in final test Is able to select a particular method and provide appropriate arguments for optimization, classification and recognition tasks. - Appropriate questions in final test. Individual practical work. Is able to apply machine learning methods that are appropriate for a particular tasks. - Appropriate questions in final test. Individual practical work. |
| Course prerequisites | Mathematics, Probability theory |