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Doctoral Programmes

Qualification

2020/2021
Academic Year
RUS
Instruction in Russian
2
ECTS credits
Delivered by:
School of Data Analysis and Artificial Intelligence
Type:
Compulsory course
When:
2 year, 1 semester

Instructor

Obiedkov, Sergei

Obiedkov, Sergei

Программа дисциплины

Полная версия программы учебной дисциплины

Аннотация

In this course, we study recent PhD theses in Computer Science, predominantly those that have obtained some international recognition. The goal is to understand what results are currently considered worthy of a PhD degree, as well as how to structure one's thesis and how to present one's research results, both orally and in a written form.
Цель освоения дисциплины

Цель освоения дисциплины

  • acquire understanding of the standards for PhD theses in the field
  • learn how to present one's work in the more general context of one's field
Планируемые результаты обучения

Планируемые результаты обучения

  • is able to present his or her research results in written and oral forms according to the current standards for such presentations
  • is able to understand the results of other researchers presented in research papers or manuscripts
  • understands the current expectations from PhD-level research results in Computer Science
Содержание учебной дисциплины

Содержание учебной дисциплины

  • Reading and writing a PhD thesis in Computer Science
    Taking as examples some of the best recent PhD theses in Computer Science, we discuss the current standards for the PhD-level research results in the filed, as well as the way they should be presented within a thesis.
Элементы контроля

Элементы контроля

  • неблокирующий Exam
  • блокирующий Pass/fail exam
Промежуточная аттестация

Промежуточная аттестация

  • Промежуточная аттестация (I семестр)
    The mark is determined through an exam, which consists of a 30-minute presentation on the student’s project followed by questions from committee members related to the research project and any of the papers in the list of references.
Список литературы

Список литературы

Рекомендуемая основная литература

  • Finn, C. (2018). Learning to Learn with Gradients. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsbas&AN=edsbas.446B409E
  • Multi-perspective process mining. (2018). Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsnar&AN=edsnar.oai.pure.tue.nl.publications.b40869c0.2d11.4016.a92f.8e4ee9cd9d66
  • S. Keshav. (2012). How to Read a Paper. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsbas&AN=edsbas.9BD51B06

Рекомендуемая дополнительная литература

  • Adamsen, C. Q. (2018). Automatiserede Testingteknikker for Event-Drevne og Dynamisk Typede Softwareapplikationer ; Automated Testing Techniques for Event-Driven and Dynamically Typed Software Applications. Denmark, Europe: Aarhus Universitet. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsbas&AN=edsbas.BC641D5C
  • Elsden, C. R. (2018). A quantified past : fieldwork and design for remembering a data-driven life. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsbas&AN=edsbas.71FB2B71