Methods of machine learning
Entry requirements: Basic knowledge of probability theory, statistics and programming.
Students will get an idea about the basic tasks and methods of machine learning and data mining, examine the statistical foundations of machine learning theory, learn how to solve the problem of classification, egression, clustering, using machine learning methods and algorithms and evaluate the quality of the solutions, learn to apply software for machine learning to solve the domain-oriented problems.
The course combines lectures on statistical theory of machine learning with a practical mastering of efficient algorithms to solve real-world problems. The course consists of three sections, covering methods of classification and regression, unsupervised learning, and neural networks. As a result of the successful completion of the course the student will acquire knowledge extraction skills useful for professional work in any field related to the accumulation and processing of large amounts of data.
Attendance is mandatory. Students cannot miss more than one class. Final assessment is carried out in the form of examination. The necessary condition to be admitted to the exam is a successful completion of all practical assignments.