This course is designed to introduce students to the current state of the art in machine learning and artificial intelligence. It combines theoretical foundations of machine learning algorithms with extensive practical exercises. The course covers material from classical algorithms to deep learning approaches and recent achievements in the field of artificial intelligence.
- Applied Statistics and Data Analysis
The course contains basic information from mathematical statistics, which will be used and supplemented in the future. The article considers classical probabilistic models of decision making about classes of observed objects according to the values of their attributes (models of classification or choice of hypotheses); it is assumed that the distributions of a feature for each class of objects are known exactly or with accuracy up to type. Goodness-of-fit criteria are discussed as a tool for testing the reliability of hypotheses and the problem of estimating distributions (in particular, the problem of so-called parametric estimation).
Methods of nonparametric estimation of distributions, which require much less a priori information about their properties, are discussed separately. The course also includes basic information on regression analysis, which is used to identify and evaluate the probabilistic relationships between the random variables under study.
- Software Development & Data Storages
This course focuses on the fundamentals of software engineering. Proper design is an important part of any project. This course covers the basics of the Python programming language, basic concepts and language constructs. Along with this, this course provides tools for using Python programming language in complex projects. You will gain insight into the correct design of the code, maintaining the codebase and integrating your applications with others.
In today’s world there is a lot of different content: videos, books, music, articles. Due to the abundance of information it is difficult for a person to choose what is interesting for one from the variety of options. Such problems are solved by recommender systems. They are present in a wide range of services: online shops, video, news feeds etc. Although we do not always notice it, recommendation algorithms have become an integral part of our lives. In this course, students will examine the classical algorithms of recommendations: from matrix factorization to modern neural network approaches.