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Regular version of the site
Bachelor 2018/2019

Research Seminar "Data Analysis and Artificial Intelligence 1"

Area of studies: Applied Mathematics and Information Science
When: 3 year, 1-4 module
Mode of studies: Full time
Language: English
ECTS credits: 4

Course Syllabus

Abstract

The discipline goal is to develop students' professional skills required for independent analytical work in applied fields of the computer science. The course consists of two parts: Bioelectrical digital signal processing and Introduction to the Semantic Web Technologies. The former aims to improve skills of students in developing their research projects related with digital signal processing. The course program includes two main parts. The first part is an introduction in digital signal processing (DSP) theory. The second part covers practical issues of DSP theory application to the problem of brain-computer interfaces development ‒ one of the major fields in modern neuroscience. The latter is a gentle introduction to the theory and practice of the Semantic Web, an extension of the current Web that provides an easier way to find, share, reuse and combine information. It is based on machine-readable information and builds on XML technology's capability to define customized tagging schemes, RDF's (Resource Description Framework) flexible approach to representing data, the OWL (Web Ontology Language) schema language and SPARQL query language. The Semantic Web provides common formats for the interchange of data (where on the Web there is only an interchange of documents). It also provides a common language for recording how data relates to real world objects, allowing a person or a machine to start off in one database, and then move through an unending set of databases which are connected not by wires but by being about the same thing. Important applications of the Semantic Web technologies include Healthcare (SNOMED CT), Supply Chain Management (Biogen Idec), Media Management (BBC), Data Integration in the Oil & Gas industry (Chevron, Statoil), Web Search and E-commerce.
Learning Objectives

Learning Objectives

  • Students will be able to use development techniques, skills and tools necessary to digital signal processing.
  • Students will understand the theoretical foundations of Semantic Technologies, including the languages RDF/S, SPARQL, the Web Ontology Language OWL.
  • Students will have practical skills of modelling data using RDF/S, querying RDF triplestores, relational databses and XML documents, building ontologies and using datalog.
  • Students will be able to communicate effectively.
  • Students will understand professional and ethical responsibility.
Expected Learning Outcomes

Expected Learning Outcomes

  • Students know the basic concepts of digital signal processing (DSP) theory.
  • Students know practical issues of DSP theory application to the problem of brain-computer interfaces development.
  • Students formulate the task and goals for an independent research and/or scientific programing system development.
  • Students prepare a presentation based on his research and/or scientific programing system.
  • Students understand the basics of first-order predicate logic, its syntax and semantics.
  • Students understand the principles of automated theorem proving with first-order predicate logic.
  • Students understand and use the logic programming language Prolog.
Course Contents

Course Contents

  • Discrete signals
    <ul><li>Analogue and discrete signals representation</li> <li>Examples of discrete signals</li> <li>Periodic discrete signals </li></ul>
  • Discrete systems
    <ul><li>Properties of the discrete systems</li> <li>Linear time-invariant (LTI) systems </li></ul>
  • LTI systems
    <ul><li>Properties of the LTI systems</li> <li>Frequency response of the LTI system</li> </ul>
  • Fourier transform
    <ul><li>Representation of the signals by Fourier transform</li></ul>
  • Discrete Fourier transform
    <ul><li>Representation of the periodic signals by discrete Fourier transform</li></ul>
  • Basic DSP problems
    <ul><li>Solution of the basic DSP problems</li></ul>
  • Python scipy.signal package
    <ul><li>Spectral Analysis</li> <li>Filter design </li></ul>
  • Introduction to bioelectrical interfaces design
    <ul><li>General framework of brain-computer interface (BCI) systems</li> <li>Real-time encephalography paradigm </li></ul>
  • Practical example 1
    <ul><li>Attention detector</li></ul>
  • Spatial filtering
    <ul><li>Common spatial pattern</li> <li>Independent Component Analysis </li></ul>
  • Practical example 2
    <ul><li>Motor-imagery BCI</li></ul>
  • Brain state classification problem
    <ul><li>Machine learning techniques for BCI</li></ul>
  • First-Order Predicate Logic
  • Normal Forms and Unification
  • Resolution Proofs
  • Resolution as Computation
  • Horn Programs
  • Pre-defense of the course work
    The last part of the course includes a pre-defense of the course work.
Assessment Elements

Assessment Elements

  • non-blocking Written exam (1st part)
    Written exam is aimed at evaluating student understanding of the main DSP theory topics.
  • non-blocking Programming project (1st part)
    Programming project is aimed at developing student ability to implement DSP techniques using the Python programming language and apply it to the practical problems.
  • non-blocking Midterm test (2nd part)
    One test in the middle of the second semester on the lectures materials.
  • non-blocking Written exam (2nd part)
    The final control is presented only in the end of the second part of the Scientific Seminar in the form of a written exam. Students must demonstrate knowledge of the material covered during the course.
  • non-blocking Presentation (3rd part)
    Students should prepare a presentation on the results obtained in the framework of the course work.
Interim Assessment

Interim Assessment

  • Промежуточная аттестация (4 модуль)
    0.25 * Midterm test (2nd part) + 0.2 * Presentation (3rd part) + 0.18 * Programming project (1st part) + 0.12 * Written exam (1st part) + 0.25 * Written exam (2nd part)
Bibliography

Bibliography

Recommended Core Bibliography

  • Wolpaw, J., Wolpaw, E. W. (ed.). Brain-computer interfaces: principles and practice. – Oxford Univ. Press, 2012.

Recommended Additional Bibliography

  • Staab S., Studer R. Handbook on ontologies. – Springer, 2009. – 811 pp.