| 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 study course addresses the question of how to enable computers to learn from past experiences. The main theories of artificial intelligence, statistics, information, etc. are discussed. terms and techniques to the extent applicable to machine learning.. |
||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Contents |
|
||||||||||||||||||||||||||||||||||||||||||||||||||||||
|
Goals and objectives of the course in terms of competences and skills |
The aim of the study course is to provide knowledge of the most important algorithms and theories that form the basis of machine learning, as well as to provide appropriate practical skills. Tasks of the study course are to provide knowledge and skills: - to apply the machine learning techniques covered in the course using Python or an equivalent programming language; - to apply software tools and libraries appropriate to the methods; - to be able to identify the appropriate method and tool for a specific problem; - to be able to debug the code implementing the method, as well as to adjust the hyperparameters of specific methods; - to be able to interpret the obtained results and make decisions on the compliance of the applied method and hyperparameter values with the expected result. | ||||||||||||||||||||||||||||||||||||||||||||||||||||||
|
Learning outcomes and assessment |
Is able to describe the main principles, advantages and limitations of machine learning. - Appropriate questions in the exam. Is able to apply and finetune clusterization. - Appropriate questions in the exam. Individual practical work. Is able to apply and finetune classification methods. - Appropriate questions in the exam. Individual practical work. Is able to apply and finetune artificial neural networks. - Appropriate questions in the exam. Individual practical work. Is able to apply and finetune text analysis algorithms. - Appropriate questions in the exam. Individual practical work. Is able to apply and finetune genetic optimization methods. - Appropriate questions in the exam. Individual practical work. Is ably to apply time series analysis and classification methods. - Appropriate questions in the exam. Individual practical work. |
||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Evaluation criteria of study results |
Individual practical work - 50%
Exam - 50% |
||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Course prerequisites | Mathematics, probability theory. | ||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Course planning |
|
||||||||||||||||||||||||||||||||||||||||||||||||||||||