PhD courses
Speciality Code:
8D06102
Speciality Name:
Computer Engineering
Faculty:
Information technology
Qualification:
- Scientific and pedagogical direction - Doctor of Philosophy (PhD)
- Model of graduating student
- Mandatory disciplines
- Elective disciplines
- Professional
Upon completion of this educational program, it is expected that undergraduates will be able to:
ON1 put into practice modern tools of intellectual analysis;
ON2 apply methods for analyzing and calculating the reliability of hardware and software, methods for ensuring and improving the reliability of systems;
ON3 formalize the task of monitoring security parameters by information systems;
ON4 make mathematical models for high-performance computing, to perform software implementation using modern supercomputer technologies;
ON5 produce and synchronize calculations immediately on a large number of computing units;
ON6 check the program for compliance with the formal specification with the use of formal verification methods;
ON7 perform verification of the properties of models in order to identify the shortcomings of the modeling process;
ON8 manage IT projects using modern software in the industry;
ON9 critically analyze, consolidate and expand data analysis concepts to create innovative analytics systems for big data;
ON10 plan complex deployments of large data systems and make informed judgments based on in-depth in-depth knowledge of specialized cloud solutions;
ON11 develop real-time applications for cloud services and mobile platforms;
ON12 implement algorithms and network applications, client-server and P2P algorithms.
ON1 put into practice modern tools of intellectual analysis;
ON2 apply methods for analyzing and calculating the reliability of hardware and software, methods for ensuring and improving the reliability of systems;
ON3 formalize the task of monitoring security parameters by information systems;
ON4 make mathematical models for high-performance computing, to perform software implementation using modern supercomputer technologies;
ON5 produce and synchronize calculations immediately on a large number of computing units;
ON6 check the program for compliance with the formal specification with the use of formal verification methods;
ON7 perform verification of the properties of models in order to identify the shortcomings of the modeling process;
ON8 manage IT projects using modern software in the industry;
ON9 critically analyze, consolidate and expand data analysis concepts to create innovative analytics systems for big data;
ON10 plan complex deployments of large data systems and make informed judgments based on in-depth in-depth knowledge of specialized cloud solutions;
ON11 develop real-time applications for cloud services and mobile platforms;
ON12 implement algorithms and network applications, client-server and P2P algorithms.
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PhD thesis writing and defence
- Number of credits: 12
- Type of control: Докторская диссертация
- Description: The main purpose of "PhD thesis writing and defence": of a doctoral dissertation is the formation of the doctoral students' ability to disclose the content of research work for the defense of the thesis. During the study of course, doctoral student's should be competent in: 1. to substantiate the content of new scientifically grounded theoretical and experimental results that allow to solve a theoretical or applied problem or are a major achievement in the development of specific scientific directions; 2. explain the assessment of the completeness of the solutions to the tasks assigned, according to the specifics of the professional sphere of activity; 3. they can analyze alternative solutions for solving research and practical problems and assess the prospects for implementing these options; 4. apply the skills of writing scientific texts and presenting them in the form of scientific publications and presentations. 5. to plan and structure the scientific search, clearly highlight the research problem, develop a plan / program and methods for its study, formalize, in accordance with the requirements of the State Educational Establishment, the scientific and qualification work in the form of a thesis for a scientific degree Doctor of Doctor of Philosophy (PhD) on specialty «8D07502 – Standardization and certification (by industry)». During the study of the discipline doctoral student will learn following aspects: Registration of documents for presentation of the thesis for defense. Information card of the dissertation and registration-registration card (in the format Visio 2003). Extract from the minutes of the meeting of the institution, in which the preliminary defense of the thesis took place. Cover letter to the Higher Attestation Commission. Expert conclusion on the possibility of publishing the author's abstract. Expert opinion on the possibility of publishing a dissertation. Minutes of the meeting of the counting commission. Bulletin for voting. A shorthand record of the meeting of the dissertational council. List of scientific papers. Response of the official opponent. A review of the leading organization. The recall of the scientific adviser.
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Fundamental bases of the distributed intellectual systems
- Number of credits: 5
- Type of control: [RK1+MT+RK2+Exam] (100)
- Description: The main purpose of the discipline: studying the main features of the analysis and design of systems using a distributed approach. During the study of course, doctor students should be competent in: - describe the main approaches to the development of multiagents programs; - apply methods of computing for computational mathematics; - apply general schemes for the development of programs for implementing specific algorithms; - develop programs using modern technologies of programming; - to build a model for executing programs; - calculate the main parameters of programs, and evaluate the effectiveness of computations. During the study of the discipline doctor students will learn following aspects: Basic concepts of the theory of agents. Modern approaches to solving distributed tasks. General classification agents. General characteristics of multi-agent systems. Models of collective behavior. The types of models. Models of societies of agents. Architecture of agents based on knowledge. IDS architecture. WILL-architecture. InteRRaP architecture.
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Academic writing
- Number of credits: 2
- Type of control: [RK1+MT+RK2+Exam] (100)
- Description: This course covers basic skills related to the logic of building text, i.e. requirements for building abstracts and arguments, the main formats of work in English depending on the discipline, requirements for annotations as a special type of small work, recommendations on stylistics and the rules of English punctuation. Publications in international journals: critical assessment of information sources, editing, expert reviews, material requirements and evaluation criteria, practical examples, the concept of plagiarism. Presentation of written work: presentation structure, framing presentation elements (ways to attract audience attention, logical connectives and transitions), rhetorical techniques and classic examples from modern practice of public speaking, rules for the design of slides, interaction with the audience, answers to questions.
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Scientific Research methods
- Number of credits: 3
- Type of control: [RK1+MT+RK2+Exam] (100)
- Description: The purpose of this discipline is to master the basics of the methodology of scientific research, consideration of different levels of scientific knowledge. Study of the stages of conducting research, including the selection of the direction of research, staging of scientific and technical problems, conducting theoretical and experimental research, recommendations for formalization of the formulation The course is also aimed at reviewing the basics of inventive work, patent search and sample plan for a PhD dissertation
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Advanced machine learning
- Number of credits: 5
- Type of control: [RK1+MT+RK2+Exam] (100)
- Description: The main purpose of the discipline: the formation of the basic competencies of the data analyst, among which an adequate choice of the method for solving a particular problem of data analysis, preprocessing data, setting parameters of the analysis method and interpreting the results obtained. During the study of course, doctor students should be competent in: - state the basic principles, methods and tasks of machine learning; - develop algorithms and write programs for logical, probabilistic, metric models of machine learning; - to compare and select an acceptable method of machine learning for each specific case; - solve practical problems of data analysis using methods of intellectual, statistical and visual analysis; - apply data manipulation methods, methods of data validation in applications; - deploy data warehouses. During the study of the discipline doctor students will learn following aspects: Logical methods: deciduous trees and deciduous forests. Metric methods of classification. Linear methods, stochastic gradient. SVM. Logical regression. Classification quality metrics. Linear regression. Reducing dimension is the main component method. Composition of algorithms, gradient bouting. Neural networks. Clusterization and visualization. Part-time training. Application data analysis: submission and solution.
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Mobile and wireless Communications
- Type of control: [RK1+MT+RK2+Exam] (100)
- Description: The main purpose of the discipline: increasing knowledge in the field of design and assessment of user interfaces for mobile devices, and also studying of a design philosophy and application development on mobile and a web platforms. During the study of course, doctor students should be competent in: - map various libraries, services, push notifications, Core Data, Pod, UI-design, MVC, which are used in the development of mobile applications; - use libraries to create web page templates and session management; - develop Web components that can modify data and carry out temporary storage; - interact with databases and Web services; - display content in response to customer requests; - create and run simple programs based on mobile and web platforms. During the study of the discipline doctor students will learn following aspects: Overview of the web and mobile platforms. Programming with the help of platform-dependent API. Overview of platform languages (Objective C, HTML5). Languages of web programming (HTML5, Java Script, PHP, CSS). Web Platform Restrictions. Software as a Service (SaaS). Web standards. Programming languages for mobile applications. Problems with mobile and wireless communication. The performance / power ratio of mobile applications. Limitations of mobile platforms.
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Artificial Neural Networks
- Type of control: [RK1+MT+RK2+Exam] (100)
- Description: The main purpose of the discipline: to introduce the fundamental principles of the construction and functioning of artificial intelligence systems; with technologies and tools for designing and developing artificial intelligence systems. During the study of course, doctor students should be competent in: - formulate functions, classify artificial intelligence systems; - state current trends in the field of artificial intelligence; - choose mathematical methods, technologies and tools for designing, software for the development of artificial intelligence systems; - construct a domain model using knowledge representation tools, select an acceptable method of solution search; - perform system analysis, design, coding, debugging and testing; - carry out documentation and release of the software product. During the study of the discipline doctor students will learn following aspects: Introduction to artificial intelligence. Intellectual agents. Introduction to Prolog programming language. Prolog programming language: lists, graphs. Uninformed search. Heuristic search. Logical agents. First-order logic. Logical inference. Uncertainty. Training. Heuristic algorithms of search and planning, formalisms of representation of knowledge and reasoning, methods of machine learning.
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Performance Analysis of Computer and Communication Systems
- Type of control: [RK1+MT+RK2+Exam] (100)
- Description: The main purpose of the discipline: to teach the basics of designing and supporting networks, to outline the existing principles of solving security problems in networks. During the study of course, doctor students should be competent in: - configure the network; - to select the necessary equipment for the networks; - design computer networks; - conduct verification and diagnostics of network failures; - perform system analysis, design, coding, debugging and testing; - choose an acceptable security policy. During the study of the discipline doctor students will learn following aspects: Communication through the network. Functionality and protocols of the OSI application layer. The transport layer of the OSI model. The network layer of the OSI model. Dynamic routing protocols. Dynamic RIP protocol, version 1. VLSM and CIDR. Dynamic RIP protocol, version 2. Dynamic routing protocol OSPF. Technologies of global networks. Remote access. Network security.
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Advanced Cryptography
- Type of control: [RK1+MT+RK2+Exam] (100)
- Description: The main purpose of the discipline: to outline the fundamental principles of information protection with the help of cryptographic methods and examples of implementing these methods in practice, presenting the foundations of a systematic approach to organizing the protection of information transmitted and processed by means of cryptographic methods; the development of ciphers; illumination of mathematical methods used in cryptography. During the study of course, doctor students should be competent in: - define the mechanisms for managing the host and network access (for example, the access control list); - implement information security systems for computer systems and networks; - ensure the protection of information and information objects using standards and protocols of network interaction; - apply cryptography methods when solving information protection tasks, implement software implementation of cryptographic algorithms, conduct analysis of cryptosystems durability; - conduct development of network security tools of the organization; - conduct design and maintenance of network security tools of the organization. During the study of the discipline doctor students will learn following aspects: Introduction to cryptography. Basic concepts and definitions. History of cryptography. Historical ciphers. Mathematical model of cipher. The theory of secrecy Shannon. Block ciphers. Pseudo-random sequences and stream ciphers. The Simmons Simulation Theory and cryptographic hash functions. Asymmetric (with public key) ciphers. Digital signature schemes.
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Advanced Parallel Programming
- Type of control: [RK1+MT+RK2+Exam] (100)
- Description: The main purpose of the discipline: to give doctor students a general idea of parallel computing technologies and their application. During the study of course, doctor students should be competent in: - describe the main approaches to the development of parallel programs, methods of parallel computing for computational mathematics; - apply general schemes for the development of parallel programs for implementing specific algorithms; - develop parallel programs using MPI and OpenMP technologies; - build a model for executing parallel programs; - calculate the main parameters of parallel programs; - evaluate the effectiveness of parallel computations. During the study of the discipline doctor students will learn following aspects: Principles of construction of parallel computing systems. Modeling and analysis of parallel computations. Principles of developing parallel algorithms and programs. Systems for developing parallel programs. Parallel numerical algorithms for solving typical problems of computational mathematics.
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Distributed Structure of Computing Systems (SRI)
- Type of control: [RK1+MT+RK2+Exam] (100)
- Description: The main purpose of the discipline: training of specialists with fundamental knowledge and practical skills in the field of building distributed information systems and networks, software engineering, the general theory of constructing mathematical models and their implementation, in-depth knowledge of the basics of computer science, theory and practice of project management on the creation of distributed information systems. During the study of course, doctor students should be competent in: - apply main classes of architectures of modern high-performance computing systems; - use the principles of development of parallel algorithms; - apply principles of improving the performance of computing systems; - use main technologies of parallel programming; - develop the skills to develop parallel programs for solving professional problems; - design distributed structure. During the study of the discipline doctor students will learn following aspects: Problem-oriented distributed information systems. Distributed processing of information in automated systems. The architecture of distributed information systems. Technological base of distributed information systems. Distributed information resources and networks. Distributed file systems, databases and databases. Technology of building network software. Managing the exchange of information in distributed information systems. Telecommunication environments. Methods, tools and protocols for accessing the environment and remote information resources. Multiprocessor network devices. Interfaces and protocols for communication with the object.Technology for the design of distributed information systems. The concept of a transaction. Distributed transactions. Distributed system of CORBA objects. DCOM technology. Trends in distributed systems.
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Pedagogical
- Type of control: Защита практики
- Description: Aim оf discipline: formation of the ability to carry out educational activities in universities, to design the educational process and conduct certain types of training sessions using innovative educational technologies.
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Research
- Type of control: Защита практики
- Description: The purpose of the practice: gaining experience in the study of an actual scientific problem, expand the professional knowledge gained in the learning process, and developing practical skills for conducting independent scientific work. The practice is aimed at developing the skills of research, analysis and application of economic knowledge.