Intro in ML

The course is an introduction to machine learning (ML) and its applications. It covers fundamental topics of ML and describes the most important algorithmic principles and approaches, as well as the aspects of the algorithms application.

The course starts with the review of canonical applications and ML problems, learning scenarios, etc. Then, fundamental ML algorithms for classification, regression, clasterization, their properties and practical applications are discussed. The last part of the course is devoted to the advanced topics of ML, such as Gaussian processes and neural networks.

Within the practical sessions we show how to use ML methods and tune their hyperparameters. Homeworks include the application of existing algorithms to data analysis problems.

Students are assumed to be familiar with the basic concepts of linear algebra, probability theory, mathematical analysis, optimization theory and Python.

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