Applied Artificial Intelligence Master’s courses

More information you can find here Базовый трек на 1 русском языке / Семестр 1

More information you can find here Базовый трек на 1 русском языке / Семестр 2

More information you can find here Базовый трек на 2 русском языке / Семестр 1

More information you can find here Базовый трек на 2 русском языке / Семестр 2

More information you can find here Продвинутый трек 1 на русском языке / Семестр 1

More information you can find here Продвинутый трек 1 на русском языке / Семестр 2

More information you can find here Продвинутый трек 2 на русском языке / Семестр 1

More information you can find here Продвинутый трек 2 на русском языке / Семестр 2

More information you can find here Advanced Track 3 in Russian / Семестр 1

More information you can find here Advanced Track 3 in Russian / Семестр 2

Data Preprocessing and Elements of Statistics

The discipline comprises two parts:

1. The basic techniques of data preprocessing, such as data types and sources, data quality evaluation and methods of quality improvement, data transformation and data preparation for future exploratory analysis are discussed in the first part, i.e. "Data preprocessing". Then, the following problems are observed: data visualization and data normalization methods, descriptive statistics, the objective function construction and working with time series.
2. The part "Elements of Statistics" gives the students practical skills of statistical data processing to be used for solving analytical problems in both personal and professional scopes. First of all, we will consider the most important concepts of probability theory, which are the baseline for many statistical constructions and conclusions. We will discuss what are random events and random variables, how to construct a distribution function and how to calculate the probability of occurrence of various events, what are the characteristics of most common distributions that are encountered in every day life. There are practical examples of sample processing and calculating of the most important estimates of general population, such as: mean, variance, distribution density, two random variables correlation, as well as the properties of these estimates. In addition, students will learn how to construct confidence intervals for estimating parameters of various distributions: how accurate the estimates obtained from the sample are, and whether they are suitable in real life cases. In conclusion, the hypothesis testing is considered.

Data Preprocessing and Statistics with R

The course consists of two parts:

1. The basic techniques of data preprocessing, such as data types and sources, data quality evaluation and methods of quality improvement, data transformation and data preparation for future exploratory analysis are discussed in the first part, i.e. "Data preprocessing". Then, the following problems are observed: data visualization and data normalization methods, descriptive statistics, the objective function construction and working with time series.
2. The second part "Statistics with R" gives the students practical skills of statistical data processing with R to be used for solving analytical problems in both personal and professional scopes. First of all, we will consider the most important concepts of probability theory, which are the baseline for many statistical constructions and conclusions. We will discuss what are random events and random variables, how to construct a distribution function and how to calculate the probability of occurrence of various events, what are the characteristics of most common distributions that are encountered in every day life. There are practical examples of sample processing and calculating of the most important estimates of general population, such as: mean, variance, distribution density, two random variables correlation, as well as the properties of these estimates. In addition, students will learn how to construct confidence intervals for estimating parameters of various distributions: how accurate the estimates obtained from the sample are, and whether they are suitable in real life cases. In conclusion, the hypothesis testing is considered.

Big Data: Storage Technologies and Elements of Statistics

The course consists of two parts:

1. The first part "Big Data: Storage Technologies" discusses how to organize big data storage using relational databases and NoSQL storages. The methods of designing data structures, data query language and processing techniques of structured and semi-structured data are studied.
2. The second part "Elements of Statistics" gives the students practical skills of statistical data processing to be used for solving analytical problems in both personal and professional scopes. First of all, we will consider the most important concepts of probability theory, which are the baseline for many statistical constructions and conclusions. We will discuss what are random events and random variables, how to construct a distribution function and how to calculate the probability of occurrence of various events, what are the characteristics of most common distributions that are encountered in every day life. There are practical examples of sample processing and calculating of the most important estimates of general population, such as: mean, variance, distribution density, two random variables correlation, as well as the properties of these estimates. In addition, students will learn how to construct confidence intervals for estimating parameters of various distributions: how accurate the estimates obtained from the sample are, and whether they are suitable in real life cases. In conclusion, the hypothesis testing is considered.

Introduction to Machine Learning (tools) and Applied Artificial Intelligence in Science and Business

The course consists of two parts:

1. The first part "Introduction to Machine Learning (tools)" discusses different types of machine learning, demonstrates practical examples how to solve various problems with machine learning methods. The main focus is on regression problems, classification and clustering problems tasks.
2. The second part "Applied artificial Intelligence in Science and Business" introduces students to the methods and technologies of knowledge engineering, intelligent security technologies, including biometrics, vulnerability prediction, cyberspace management. The artificial intelligence applications in the speech synthesis and recognition, image and texts processing is also considered.

Data Preprocessing and Big Data: Storage Technologies

The discipline comprises two parts:

1. The basic techniques of data preprocessing, such as data types and sources, data quality evaluation and methods of quality improvement, data transformation and data preparation for future exploratory analysis are discussed in the first part, i.e. "Data preprocessing". Then, the following problems are observed: data visualization and data normalization methods, descriptive statistics, the objective function construction and working with time series.
2. The second part "Big Data: Storage Technologies" discusses how to organize big data storage using relational databases and NoSQL storages. The methods of designing data structures, data query language and processing techniques of structured and semi-structured data are studied.

Statistics with R and Introduction to Machine Learning (tools)

The course consists of two parts:

1. The first part "Statistics with R" gives the students practical skills of statistical data processing with R to be used for solving analytical problems in both personal and professional scopes. First of all, we will consider the most important concepts of probability theory, which are the baseline for many statistical constructions and conclusions. We will discuss what are random events and random variables, how to construct a distribution function and how to calculate the probability of occurrence of various events, what are the characteristics of most common distributions that are encountered in every day life. There are practical examples of sample processing and calculating of the most important estimates of general population, such as: mean, variance, distribution density, two random variables correlation, as well as the properties of these estimates. In addition, students will learn how to construct confidence intervals for estimating parameters of various distributions: how accurate the estimates obtained from the sample are, and whether they are suitable in real life cases. In conclusion, the hypothesis testing is considered.
2. The second part "Introduction to Machine Learning (tools)" discusses different types of machine learning, demonstrates practical examples how to solve various problems with machine learning methods using tools without programming. The main focus is on regression problems, classification and clustering problems tasks.

Elements of Statistics and Introduction to Machine Learning (tools)

The course consists of two parts:

1. The first part "Elements of Statistics" gives the students practical skills of statistical data processing to be used for solving analytical problems in both personal and professional scopes. First of all, we will consider the most important concepts of probability theory, which are the baseline for many statistical constructions and conclusions. We will discuss what are random events and random variables, how to construct a distribution function and how to calculate the probability of occurrence of various events, what are the characteristics of most common distributions that are encountered in every day life. There are practical examples of sample processing and calculating of the most important estimates of general population, such as: mean, variance, distribution density, two random variables correlation, as well as the properties of these estimates. In addition, students will learn how to construct confidence intervals for estimating parameters of various distributions: how accurate the estimates obtained from the sample are, and whether they are suitable in real life cases. In conclusion, the hypothesis testing is considered.
2. The second part "Introduction to Machine Learning (tools)" discusses different types of machine learning, demonstrates practical examples how to solve various problems with machine learning methods using tools without programming. The main focus is on regression problems, classification and clustering problems tasks.

Big Data: Storage Technologies and Introduction to Machine Learning (Python)

The discipline consists of two parts:

1. The first part "Big Data: Storage Technologies" discusses how to organize big data storage using relational databases and NoSQL storages. The methods of designing data structures, data query language and processing techniques of structured and semi-structured data are studied.
2. The second part "Introduction to Machine Learning (Python)" discusses different types of machine learning, demonstrates practical examples how to solve various problems with machine learning methods using the Python programming language. The main focus is on regression problems, classification and clustering problems tasks.

Advanced Machine Learning (Python) and Automatic Text Processing

The discipline consists of two parts:

1. The first part "Advanced Machine Learning (Python)" shows how to reduce the feature set dimension in factor analysis. Further, the support vector machines, decision trees, model ensembles and another type of machine learning i.e. reinforcement learning are considered.
2. The second part "Automatic Text Processing" discusses the algorithms and tools of natural language processing. After a brief introduction to the history of the field, various approaches are considered that process texts at different levels : from tokenization to parsing. The main focus is on algorithms that use machine learning for text processing. After finishing this part of the discipline, students will have an idea of the landscape of modern automatic text analysis methods and will try several most common tools in practice, such as pymorphy2, mystem, NLTK, scikit-learn, UDPipe, etc.

Introduction to Machine Learning (Python) and Advanced Machine Learning (Python)

The discipline consists of two parts:

1. The part "Introduction to Machine Learning (Python)" discusses different types of machine learning, demonstrates practical examples how to solve various problems with machine learning methods using the Python programming language. The main focus is on regression problems, classification and clustering problems tasks.
2. The part "Advanced Machine Learning (Python)" shows how to reduce the feature set dimension in factor analysis. Further, the support vector machines, decision trees, model ensembles and another type of machine learning i.e. reinforcement learning are considered.

Automatic Text Processing and Image Processing

The discipline consists of two parts:

1. The first part "Automatic Text Processing" discusses the algorithms and tools of natural language processing. After a brief introduction to the history of the field, various approaches are considered that process texts at different levels : from tokenization to parsing. The main focus is on algorithms that use machine learning for text processing. After finishing this part of the discipline, students will have an idea of the landscape of modern automatic text analysis methods and will try several most common tools in practice, such as pymorphy2, mystem, NLTK, scikit-learn, UDPipe, etc.
2. The part "Image Processing" shows basic algorithms of image analysis. Various ways of digital representation of images and color models are considered. Image transformations (intensity and color) and image filtering (both spatial and frequency) are discussed. Besides, students will learn how to use various types of neural networks (such as Alex Net, Rus news, Ngs, Inception) for image classification, as well as two-phase and single-phase algorithms: YOLO, SSD, Mask-R-CNN.