|Name||Computer Aided Solution Processing|
|Status||Compulsory/Courses of Limited Choice|
|Level and type||Post-graduate Studies, Academic|
|Field of study||Computer Science|
|Academic staff||Gints Jēkabsons, Jurijs Lavendels|
|Credit points||4.0 (6.0 ECTS)|
In the lecture course, students learn elements of supervised machine learning and mathematical optimization with emphasis on regression methods and combinatorial optimization. The course also discusses methods for estimation of machine learning model's predictive performance, feature selection, optimization of model's structure, as well as practical applications of the methods..
Goals and objectives
of the course in terms
of competences and skills
|The goal of the lecture course is for the students to gain knowledge on the principles of supervised machine learning, principles of regression, specific machine learning and optimization methods, evaluation of predictive models and optimization of their structure as well as to gain knowledge and skills in practical application of the covered methods.|
Understands the fundamental machine learning problems considered in the lecture course as well as understands how to solve them using specific methods. - Successfully answered tests and passed examination.
Is able to do own research on problems connected with the topics of the lecture course and develop own solutions as well as implement them in software. - Positively evaluated written report.
Is able to use methods and algorithms appropriate for the problems at hand. Is familiar with their operation and is able to apply them in practice. - Successfully completed individual assignments.
|Course prerequisites||Mathematics, Theory of probability and mathematical statistics|