“Data analysis and machine learning” - course 120,000 rubles. from MSU, training 48 weeks. (12 months), Date: February 16, 2023.
Miscellaneous / / November 27, 2023
The professional retraining program “Data Analysis and Machine Learning” is aimed at training specialists in the field of computer technologies capable of developing software systems using data mining and machine training.
Formation of professional competencies among students related to applied programming and databases data required to acquire the qualification “specialist in the field of data analysis and machine training"
The learning process uses the Python programming language, the Jupiter interactive development environment, scikit-learn software libraries for machine learning, and others.
Machine Learning is a broad subfield of artificial intelligence that studies methods for constructing algorithms that can learn. Machine learning is the main modern approach to data analysis and building intelligent information systems. Machine learning methods underlie all computer vision methods and are actively used in image processing. The course contains many practically applicable algorithms.
APPLICATION REQUIREMENTS
Applicants to the retraining program must have a higher or secondary specialized education. Experience programming in procedural languages is desirable.
TRAINING MODE
The program is designed for 1 year of study: from February 16, 2023 to January 31, 2024.
Volume 684 hours.
Acceptance of documents from December 20 to February 28.
Classes without reference to a schedule according to an individual educational trajectory.
To obtain a Moscow State University Diploma in professional retraining, you must complete the curriculum and prepare a final thesis.
The final work is an independent development of a software system.
1. To enroll in the program, you must fill out the following documents (by hand or electronically) and send them to [email protected]:
2. Based on the submitted documents, a Training Agreement will be prepared.
3. After signing the contract, documents for payment are sent: August-September.
4. After payment you begin training.
Professor of the Department of Information Security, Head. ICU laboratory
Academic degree: Doctor of Technical Sciences. sciences
Sukhomlin Vladimir Aleksandrovich, Honored Professor of Moscow State University, Professor, Doctor of Technical Sciences, Head of the Laboratory of Open Information Technologies (OIT).
The candidate's dissertation was defended in the field of physical and mathematical sciences at the Academic Council of the VMK in 1976.
In 1989 defended his doctoral dissertation in the specialty 05.13.11 at the Council at the Institute of Computer Science and Technology of the USSR Academy of Sciences, the topic of the dissertation is related to the modeling of complex radio engineering systems.
In 1992 awarded the academic title of professor.
Awarded the commemorative medal “800 Years of Moscow”.
In 2000-2002 developed the concept and state standards of a new scientific and educational direction “Information Technologies”. Based on these developments by the Russian Ministry of Education in 2002. direction 511900 “Information Technologies” was created and an experiment was conducted to implement it. In 2006, this direction was renamed at the initiative of the author into “Fundamental Informatics and Information Technologies” (FIIT). Currently, this direction is being implemented in more than 40 universities in the country.
Sukhomlin V.A. - developer of state standards for bachelor and master of the 2nd and 3rd generation for the direction of “Fundamental computer science and information technology”.
INTRODUCTION TO ARTIFICIAL INTELLIGENCE
The goal of the course is to give students a broad overview of artificial intelligence problems and methods.
Lecture 1.1
Logical inference methods
Lecture 1.2
Finding solutions, planning, scheduling
Lecture 1.3
Machine learning
Lecture 1.4
Human-machine interaction
PROGRAMMING IN PYTHON
The purpose of studying the discipline is to master the tools and methods of software development using the Python language and its libraries.
Lecture 2.1
Application structure
Lecture 2.2
Overview of the most important Python standard library modules and packages
Lecture 2.3
Objects and Classes in Python
Lecture 2.4
Elements of Functional Programming in Python
Lecture 2.5
Generators. Iterators
Lecture 2.6
Multithreaded Programming
Lecture 2.7
Network programming
Lecture 2.8
Working with the database
DISCRETE MATHEMATICS11
The course material is divided into five sections: Mathematical tools; Sequences; Graphs; Boolean functions; Coding theory.
Lecture 3.1
Topic 1.1. Language of mathematical logic
Lecture 3.2
Topic 1.2. Sets
Lecture 3.3
Topic 1.3. Binary relationships
Lecture 3.4
Topic 1.4. Method of mathematical induction
Lecture 3.5
Topic 1.5. Combinatorics
Lecture 3.6
Topic 2.1. Recurrence relations
Lecture 3.7
Topic 3.1. Types of graphs
Lecture 3.8
Topic 3.2. Weighted graphs
Lecture 3.9
Topic 4.1. Representation of Boolean Functions
Lecture 3.10
Topic 4.2. Boolean Function Classes
Lecture 3.11
Topic 5.1. Coding theory
THEORY OF PROBABILITY AND MATHEMATICAL STATISTICS
Lecture 4.1
Topic 1.1. Concept of probability
Lecture 4.2
Topic 1.2. Elementary theorems
Lecture 4.3
Topic 1.3. Random variables
Lecture 4.4
Topic 2.1. Statistical data processing
Lecture 4.5
Topic 2.2. Problems of mathematical statistics
MACHINE LEARNING METHODS
The course examines the main tasks of learning by precedent: classification, clustering, regression, dimensionality reduction. Methods for solving them are being studied, both classical and new, created over the past 10–15 years. Emphasis is placed on a thorough understanding of the mathematical foundations, relationships, strengths, and limitations of the methods discussed. Theorems are mostly given without proof.
Lecture 6.1
Mathematical Foundations of Machine Learning
Lecture 6.2
Basic concepts and examples of applied problems
Lecture 6.3
Linear classifier and stochastic gradient
Lecture 6.4
Neural networks: gradient optimization methods
Lecture 6.5
Metric Classification and Regression Methods
Lecture 6.6
Support Vector Machine
Lecture 6.7
Multivariate Linear Regression
Lecture 6.8
Nonlinear regression
Lecture 6.9
Model selection criteria and feature selection methods
Lecture 6.10
Logical classification methods
Lecture 6.11
Clustering and partial training
Lecture 6.12
Applied Machine Learning Models
Lecture 6.13
Neural networks with unsupervised learning
Lecture 6.14
Vector representations of texts and graphs
Lecture 6.15
Ranking training
Lecture 6.16
Recommender systems
Lecture 6.17
Adaptive forecasting methods