DSP713 Machine learning

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..
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
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

[Extended course information PDF]